Target tracking using wireless sensor networks offers multiple challenges because it usually involves intensive computation and requires accurate methods for tracking and energy consumption. Above all, scalability, energy optimization, efficiency, and overhead reduction are some among the key tasks for any protocol designed to perform target tracking using large scale sensor networks. Border surveillance systems, on the other side, need to report border crossings in a real time manner. They should provide large coverage, lower energy consumption, real time crossing detection, and use efficient tools to report crossing information. In this paper, we present a scheme, called Border Cooperative and Predictive Tracking protocol (BCTP), capable of energyaware surveillance and continuous tracking of objects and individuals’ crossing a country border and anticipating target motion within a thick strip along the border and estimating the target exit zone and time.
The large scale evolution in wireless sensor network technology makes it possible to implement wireless sensor networks (WSNs) within a wide range of applications including healthcare monitoring, battlefield surveillance, and physical or environmental conditions observation [
A target is an entity (a moving object, individual, or animal) whose state and activity are of interest for an application. Identifying a target while crossing a border by its particular features, detecting its path over a period of time and predicting its moves and positions in the future constitute the major hard tasks to address in tracking targets. In addition, tracking using sensor networks is complex due to the processing, scheduling, communication, and energy constraints it faces. In particular, the sensors should share data and cooperate to perform data fusion and event relaying appropriately.
To accomplish target tracking, the WSNs have implemented several techniques including: a) data acquisition techniques achieving the monitoring task by sampling data and measuring characteristics related to target locations and activities over time; b) scheduling techniques allowing an optimized use of energy while allowing larger sensor lifetime; and c) maintenance techniques offering an interesting level of tracking quality. One can notice that these techniques, as published in the literature, were based on the assumption that there are enough sensors that are capable of sampling targets’ features across time. Nevertheless, studies on WSNs have shown that this assumption does not hold all the time because of initial deployment weaknesses, energy consumption modes, or external factors including sensor failures and physical attacks [
Since target tracking is a realistic and dynamic estimation scheme, in its general form, an efficient tracking needs to lay down a “theory” capable of controlling the measurements, the estimation and the errors experienced within the processes of prediction and approximation. Moreover, target tracking systems in border surveillance systems should be able to comply with more constraining features, such as operating under real conditions, collaborating to help efficient interception of trespassers, and providing surveillance continuity and protection. One can notice, however, that most of the proposed solutions do not provide all this and handle only some parts of it.
In this paper, we propose a distributed and prediction capable scheme, called Border Cooperative Predictive Tracking protocol (BCTP), to accurately track mobile targets within a given area, which has the form of large thin strip along a borderline. The scheme provides a technique to deploy wireless sensor networks in the strip under a mathematically controlled random mode. The latter assumption is realistic since a similar scheme has been made available, in general, along with a mathematical control of the coverage they allow [
The contribution of this paper is three-fold: a) Given the number of targets to detect, the protocol provides a distributed mechanism for determining the set of sensors to wake up in the vicinity of the future positions of the detected targets, until complete area crossing; b) to minimize the energy consumption during tracking, the protocol reduces the number of sensors to only some among those that are expected to have the target in their range at the moment of wakeup; c) a mathematical model is developed to control tracking continuity and estimate the exit time and exit segment of any target from the strip built along the frontier; and d) a mathematical model is built to control the deployment scheme of the WSN capable of guaranteeing a high probability of tracking continuity.
The remaining part of this paper is organized as follows. Section 2 discusses the limits of the existing target tracking solutions for border surveillance and reviews the tracking techniques developed for WSNs. Section 3 develops the requirements for an efficient tracking system. Section 4 presents the Border Cooperative Predictive Tracking scheme. Section 5 builds the mathematical models for target tracking. Section 6 defines estimation techniques for exit segment determination. Section 7 constructs a mathematical model capable of determining the probability of target tracking continuity. Section 8 discusses numerical experiments of the protocol and mathematical model. Section 9 concludes this paper.
The efficiency of WSN-based surveillance systems highlights two major objectives to achieve: first, it should build on a deployment scheme capable of offering total coverage of the area under monitoring so that any target entering the area is detected. Second, it should be able to track moving objects of interest in the area. Both goals have received an extensive attention during the last decade. Efficiency also needs that sensors should optimize the energy consumption, since the aim of an efficient tracking should ensure both continuous monitoring and energy conservation. To this end, the sensors should be able to dynamically move between a sleeping status and an active status. In a target tracking application, for instance, the sensors that can detect a target, appearing in the sensor’s vicinity at a particular time, must be in an active state.
Several recent works have discussed the design of country border surveillance systems based on WSNs. A majority of the proposed solutions have displayed the WSN nodes as a linear network, so that every movement going over the barrier (or line) of the sensors is detected [
On the other hand, solutions using k-barrier coverage require that all crossing paths in the monitored region around a spatial line are covered by at least k distinct sensor nodes. Such solutions include the 3-layered hybrid network architecture for border patrol, called Bordersense that has been proposed in [
The case of border surveillance when sensors are randomly and uniformly deployed has been also investigated in a few works. Even though uniform sensor deployment is useful in theoretical scenarios, it remains unrealistic in the real cases, where a non-uniform placement is experienced due to several environmental features such as dense vegetation and rough terrain. In addition, most of the published works do not provide a mathematical control of the sensing coverage, so that coverage hole can be detected and repaired.
On the hand, the authors in [
On the other hand, one can see that some other proposed frameworks for border surveillance assumed that a line- (or a thick-line) based WSN offering barrier or k-barrier coverage is provided. Under this assumption, the solutions attempted to reduce the number of needed sensors to monitor the crossing of a given border; but, the improvement is obtained at the expense of efficiency reduction and tracking limitation. In other words, line- based deployment at best ensures the detection of all intrusion attempts, but will not allow to take an internal decision about tracking the intruders. Furthermore, the coverage provided by these solutions is not realistic due to environmental factors and sensors dropping techniques (air-dropping or manned dropping, for example). Another limitation of the proposed models is the silence they observe about the connectivity and prompt reactivity issues.
In addition, one can see that target tracking under surveillance systems presents very special constraints with respect to the common WSN-based monitoring of two dimensional areas. Indeed, tracking in border surveillance should be done in real-time mode, should be available all the time, should be continuous, and should be able to help predicting the exit time and zone for the monitored area. The objectives of the next subsection are to survey the works performed in WSN-based target tracking approaches and to discuss their limits with regards to the aforementioned differences.
We can classify the target tracking techniques into several categories, based on the sensor activation modes, the network topologies and the sensor partition techniques they use. Node activation can be naïve, randomized, selective, based on trajectory prediction, or duty cycled [
Tracking methods using naïve activation are not robust because the WSN they use is more prone to energy depletion, traffic congestion, sensor failure, and even security attacks. In the following, we consider the other solutions and mainly discuss two major overlapping categories; namely, the hierarchical and prediction-based classes of tracking [
In a hierarchical tracking method, the WSN is organized in such a way where sensors in the vicinity of a target must be able to detect it and report back to the sink node using the network hierarchy. Therefore, the sensors should be able to relay the messages of other sensor nodes, by acting as repeaters, and to support data processing results on behalf of the other senor nodes, when needed. Hierarchical tracking methods can be classified into three categories: tree-based tracking, cluster-based tracking, and hybrid methods.
In tree-based target tracking, sensor nodes are organized in a hierarchical tree (or sometime in a graph), where nodes are sensors and edges are links between sensors that can directly communicate with each other. In particular, in STUN (Scalable tracking using networked sensors), a tree-based approach in which a cost is assigned to each link, computed on the Euclidean distance between the two communicating nodes, is used [
Various other tree-based techniques have been developed [
On the other hand, cluster-based tracking methods assume that the WSN is partitioned into clusters to support collaborative tracking processes using cluster heads. Three categories of cluster-based methods can be distinguished; they are: the static clustering, dynamic clustering, and space time clustering. In the static clustering methods, the nodes are partitioned into clusters on network deployment. The cluster size, cluster head, and cluster members are static. The main advantage of a static clustering method is its simplicity. Its major disadvantage stems from the fact that it is not fault tolerant. For example, if a cluster head is out of energy, all the sensors belonging to its cluster will not be able to operate until a new cluster head is elected.
Low-Energy Adaptive Clustering Hierarchy (LEACH) was developed with the desire to reduce energy consumption in the WSN [
Alternatively, the Continuous Object Detection and Tracking (CODA) mechanism considers that the WSN uses a static backbone comprising a number of static clusters built during the initial network deployment step. In each cluster, any sensor detecting the moving targets in its vicinity transmits the detecting information directly to the cluster head [
Unlike static clustering approaches, dynamic clustering methods allow the formation of a cluster on the occurrence of certain events of interest such as the approaching of a target to the area of a group of sensors. Examples of dynamic cluster-based tracking include information-driven sensor querying (IDSQ), RARE, and DELTA [
RARE approach reduces the number of sensors contributing in tracking by preventing remote sensors from taking part in the tracking operation. It diminishes redundant information by identifying overlapping sensors; whereas DELTA builds a distributive algorithm to track a target moving at constant speed by dynamically forming a cluster and selecting the CH to reliably monitor moving targets based on light measurement.
While DELTA main drawback stems from the fact that it does not consider varying target speed, the tree- based and cluster-based methods suffer from the fact that the sensing task is usually performed by several nodes at a time. This causes heavy computation on the root node, for the tree-based schemes, and on the CH, for the others. It also makes the tree- or cluster-based WSN tracking systems lack of robustness in the case of root and CH failures.
In recent years many target tracking protocols have been proposed to provide prediction-based methods, where the next position of the target is predicted. The authors in [
In [
In [
Other target tracking schemes have assumed that the sensor nodes are mobile and the sensing coverage is not full. The main idea in these schemes is to predict the future position of the target and select one among the sensor nodes close to that position and order it to move closer to it. In particular, the authors in [
We discuss in this section special requirements of the surveillance process in terms of tracking efficiency and task scheduling.
WSN-based surveillance applications features are characterized by the nature of the environment where the applications take place and the degree of the tracking efficiency required for the applications. In the following, we discuss the most important WSN characteristics needed to achieve efficient mobile target tracking in a hazardous environment. Some of these characteristics have been discussed in [
Communication infrastructure: as mentioned before, two common types of WSN communication models are currently used to achieve surveillance: the hierarchical and the flat networks. While the first structure allows the existence of nodes, called cluster heads, capable of performing sophisticated operations and having more energy than simple sensor nodes, the nodes in the flat structure have the same capabilities and should collaborate to relay any report from the generating node to the sink node. In both models, surveillance should be able to optimize the energy consumption and keep connectivity. While hierarchical infrastructures should reduce the relaying activity within clusters, the flat architecture should reduce the number of sensors involved in event collection and target tracking.
Deployments cheme: The deployment of sensor nodes in a surveillance application in an irregular environment cannot be accomplished according to a deterministic choice. Due to the hostility of the physical environment, direct human control of the sensor nodes serving the surveillance application may be hard to achieve, and might be impossible in certain situations. Typically, sensor nodes are dropped from manned/unmanned aircrafts in a given area and should provide total sensing coverage, in some sense. Therefore, a control mechanism should be attached to the deployment scheme to allow measuring the required level of coverage any time. The control may use a large set of parameters that can be effectively monitored, including node density, sensor range, and k-coverage capability.
Energy management: The energy resources of a sensor node are limited because of the sensor size and cost constraints. In fact, localization, sensing functions, and communication techniques could not be performed without taking into consideration energy constraints. In addition, high-resolution information about the monitored area is often needed, requiring the use of energy-aware techniques. Energy is the most important factor occurring in the determination of the WSN lifetime. The required lifetime in the surveillance context should last as long as possible. Therefore, the sensors should be able to be automatically recharged after their amount of energy is consumed, or they should be replaced upon energy depletion or failure. Moreover, to provide surveillance continuity, the system should be able to estimate energy consumption for every sensor and determine in advance the energy failure moment to provide sensor replacement.
Mobile target tracking: Three major requirements have to be fulfilled by sensors in a surveillance system: First, the sensors should be unnoticeable, meaning that their positions should be hard to determine by non-autho- rized parties and their size should be limited. Second, the sensor nodes should be able to locate targets moving under a reasonable speed v in the monitored area (generally,
Monitored area coverage: The sensor nodes in a surveillance application should collaborate to achieve sensing coverage of the whole area in order to collect useful events when they occur and to build continuously the path followed by any target trying to cross the area. In addition, the WSN needs to be connected so that any collected event or target related information is forwarded to the sink node, via the other nodes if needed. Since the communication coverage of a single sensor node is basically dependent on the sensing range of the sensors attached to it, the communication requirements of the surveillance system should depend heavily on the sensor sensitivity to track targets. The WSN must be able to minimize the errors related to mobile target detection and feature measurements.
The deployment of sensors for border surveillance may be made in a strip along the borderline of a country with a width sufficiently large. The system should be able to track mobile targets (including individuals, animals, and vehicles), help following their paths, and estimate their exit points from the strip. To this end, the deployment schemes and scheduling activity of the WSN may need comply with two categories of rules: the density controlling rules and the scheduling rules. In the following, we discuss some of these rules.
Density related requirements. It is obvious that the quality of target detection and tracking depends on the number of deployed sensors and the surface of the given strip. An efficient deployment scheme should guarantee that every point in the strip, where a target can be, is sensed by at least one active sensor. However, knowing that the target enters the strip from one edge of the strip and exits it from the other edge, this condition can be achieved by the following rules:
Monitoring line rule: At least one sensing line should be active to detect the beginning of a crossing action. The sensing line is nothing but a sequence of sensors separated by at most twice the sensing range of the sensors. This line should be parallel (in some sense) to the borderline so that an object crossing the strip should traverse it. Furthermore, to provide premature detection, the sensing line should be close to the borderline. The number
of sensors that form the sensing line should be higher than
Density rule: More generally, if the coverage of every point in the strip is required, the number should be higher than
where W is the width of the strip. The formula assumes that the strip is bounded with a surface equal
Scheduling constraints. It can be easily seen that the scheduling scheme within surveillance systems should be able to comply with two contradictory objectives. They are the sensor lifetime and the sensing continuity. Three rules can be distinguished.
Lifetime rule: The lifetime of the sensors should be as long as possible, assuming that the energy consumption should be reduced for all sensors and that any target crossing should be detected. This means that the scheduling operation should handle sensor sleeping, target detection, and target tracking tasks with different strategies. The first sensors in charge of target detection should belong to at least one established line of surveillance, for instance; and in that case, the line should be maintained to provide energy reduction and modified from time to time. On the other hand, sensors in charge of tracking must be awakened only when needed.
Prediction Awake rule: Sensors in charge of tracking are awakened when the detected target occurs in their vicinity. Awake sensors in charge of tracking an active target should collaborate to predict the next positions (in the next time slot) of the detected target and reduce the number of the sensors to wake up upon target detection. The prediction of the next positions should be able to help determining which sensor(s) to wake up in the next time slot. Position prediction must take into consideration the geographic nature of the monitored area and the history of target motion.
Tracking continuity rule. Only a few sensors should be awakened to provide continuous target tracking so that targets can be monitored properly (at any time slot) until exit time. Since the time is slotted, we assume here that continuous tracking means that the surveillance system is able to locate the target at any time slot after its/his first detection. Let us notice, however, that this definition can be reduced by changing the expression “any time slot” by the expression “every n time slots”, for small values of n.
For the sake of simplicity, we suppose in this section that a WSN-based surveillance system is deployed uniformly in a thick strip along a country border, or randomly deployed with a mathematical control such as in [
The first step can be achieved assuming that a line of active sensors is configured along (and close to) the border. As soon as the target arrives to cross the line, a sensor detects it. The detection can be certainly achieved if the distance between two successive sensors in the line is smaller that
The second step is iterative. A sensor s detecting a target at time slot
Let
where
When
Distance
If the sink node receives a messages from sensors
and reporting on the positions of the target
Positions’ correlation: if the reported positions correspond to the same time slots then these position are compared. If they are different, then a new position is computed to replace the reported position (as the middle of these positions, for example) and the predicted position are revisited.
Dropping messages: the receiving sensor, say
that sensor
Let us now discuss some important features of the cooperative predictive tracking scheme. In particular, let us study the tracking continuity property of the scheme. Establishing this property requires three major constraints; namely, the sensor sensing continuity, the sensing granularity, and the target path visibility. Sensor sensing continuity assumes that every awake sensor senses its area at least once per time slot. This constraint can be reduced by requesting that the sensor should sense every m time slots (
Finally, Target path visibility constraint assumes that, if the crossing time is larger than n time slots, then the path followed by the tracked target includes more than n detected positions. This assumption guarantees that, if the time passed by a target to cross the area monitored by a sensor s is longer than a time slot, then the sensor will detect the target more than once. More generally, if the target spends more than m slots to cross the area controlled by s, then it will be detected at least m times.
The following result shows that the tracking continuity property can be achieved when the target mobility and sensing range cope with the strip width, the place of the surveillance line, and the time slot duration. The result involves a discussion on the angle
Proposition 1 (tracking continuity). Assume a target is crossing the border strip with a speed
1) The quantity
2) The width and angle
・ the deployment density is higher than
・ The width L, angle
Proof.
The first condition states that the target should be detected by one sensor on the active thick line (barrier) characterized by a certain width w.
Since an active sensor senses once in a time slot, the target speed should not exceed 2w. Therefore,
One can see that once a target is detected, the detecting sensor, say s, follows it until it gets out of its sensing area
where
Finally, the last inequality guarantees that the diagonal
The third condition implies that to provide detection of the target by a sensor in
Hence, we deduce that the WSN is able to construct the target path, from the moment it is detected until the moment it leaves the strip, provided that the two conditions of the theorem are satisfied.
Let us inform that the second condition in the theorem imposes a constraint on
In this section, we first develop a mobility model for a generic target crossing the strip. Then we establish the major features of our prediction scheme based on this model. Finally, we describe how our algorithm predicts the exit time and exit zone of the target.
To be detected, the target should move at a speed (counted in meters per time slot) higher than a given minimum speed
Target mobility rules. Assuming that the target is at position p at time slot t, a mobility rule supposes that the new position
bility rule of the target. The angle
The distances
with sensing range, the sensor sensitivity, and the slot duration. In particular, when
Target path rules: Several rules can be reasonably used to model the target path. A first rule assumes that the target approaches the exit edge of the strip with at least
This rule states again that
where
where n is the number of time slots used to observe the target,
Let
and
since
Thus, we have the following result.
Proposition 2. Assume that the aim of a target is to reach the exit edge of the border strip and that the monitoring line active has detected him/it at time slot
1) The path modeling process converges in a finite time, meaning that the target will reach the exit edge in a time shorter than a maximum time given by
2) The maximum length l of the path followed by the target between its detection point and time termination is given by
Proof.
The process has been shown to terminate by providing the computation of the maximum time to reach the exit edge
Computing the length of the target path travelled until time slot k is given by
The inequalities can be easily stated using the mobility and target path rules, knowing that
Let us now study the sequence
During step n, the predicted position of the target is replaced by the observed position
Let us now show how the sink node predicts the exit time of a target along with its exit segment, as it is kept informed by the target motion. To this end, we consider the following notations: Let
Let us compute the different segment using
・
・
・
・
・
・
where
The following result studies the evolution of the potential exit time and exit segment of the target, assuming that the distance separating the target from the exit edge decreases of at least
Proposition 3. a target crossing the border strip and
1) The length
2) The two sequences
3) If position
Proof.
First, let us notice that using the mobility rule we can show that the inclusion
This function is continuous. Let
where:
Second, one can deduce from the first statement that
The two series are finite since at every step the target approaches the edge by at least
The number of elements in these series is thus lower than
Third, it has been shown in Subsection 5.1 that if the target is detected at a position p then it reaches the edge before a maximum time
One can also easily show that the target will arrive after a time
Applying these inequalities, when
Various methods can be utilized to estimate the time of exist. One could estimate
Another method computes the average distance per slot travelled by the target and set, at time slot k, an estimation of the exit time by:
Without loss of generality, we assume in this section that the strip to monitor along the country border is a rectangle of length L and width W positioned between points
On each line the quadcopter is assumed to drop a sensor at each instant
stant when the quadcopter is at point
Taking into consideration the environment conditions of dropping and assuming that the quadcopter keeps a constant speed equal to V meters per second, one can state that the landing point of a sensor dropped at time
where
In the following we assume that
Theorem 4. Let
・
・
・
Proof.
First, let us notice that all the rectangles around
This inequality states that
Second, continuity imposes that the speed of the target
Finally, let s be a sensor tracking the target and that at time slot n, sensor realizes that the target is in position p and that it will be in a position p’ out of its tracking range. Let us consider the zone
contains a sensor. An easy computation shows that under this condition,
More generally, for given
In this section, we present the results of the simulations we have conducted. The aim of these simulations was to evaluate the performance of the target tracking method we proposed to show its efficiency, in terms of energy consumption and the exit time and zone estimation. We also evaluate respectively the scheduling messages overhead, the deployment coverage in terms of tracking continuity, and the network lifetime.
The simulation model is built by considering that the area to be monitored is of size
gy consumption. For this, we will conduct simulations by varying
Three major objectives will be studied in the sequel. First, the quality of coverage (including the barrier) will be discussed. Second, the discontinuity ratio will be analyzed as a function of important factors including
Simulations have been conducted to evaluate our deployment scheme and the quality of coverage of the barrier (i.e., the first line of surveillance along the border). The evaluation of the scheme is made by measuring the proportion of uncovered points in the area, while
One can notice that the loss of coverage for our scheme decreases when the sensing range of the sensors range increases and that all points are covered when
In a first series of simulations, we evaluate the wake up protocol overhead and measured it through the number
of additional messages due to the proposed scheduling and tracking scheme. For the deployment, we fixed the
sensing range
number x of targets per time slot from 1 to 10 and measured, for each value of the arrival number, the average number of messages generated by the sensors tracking the targets during 500 time slots. In addition, without loss
of generality, we assumed that the trespassers travel through the shortest path or the longest paths (for
The variation of the messages rate is illustrated in
ceed 60 messages for
targets attempt to cross the strip. This can be explained by the fact that a target induces the generation of 10 to 12 messages when,
The tracking continuity is studied here for width of the wake up zone equal to
Finally, we assume that during simulation a large number of targets attempt to cross the strip from random
points on the border, and that these targets follow straight lines with directions varying randomly in
side of the squares where sensors are deployed, the tracking is guaranteed until exit when
The final set of experiments discuss the size variation of the exit segment length report for the crossing targets with respect to the sensing range of the sensors and the angle
tice that, for
On the other hand, the segment length is lower than
Border surveillance systems should comply with very constraining features to be efficient. In particular, they should provide immediate detection of trespassers and guarantee tracking continuity. The system presented in this paper includes various thick lines of surveillance along the country border. It reduced the energy consumption of tracking processes by reducing properly the number of sensors to wake up for tracking. It also provides a prediction technique capable of estimating the exit time and zone of crossing targets.
The results developed within this work can be extended to encompass the security of the monitoring network, sensor fault tolerance, and coverage holes. It also can be extended to various applications such as battlefields and mining.
The author is thankful to the Tunisian Ministry for High Teaching and Scientific Research for funding the research presented in this paper.
Noureddine Boudriga, (2016) A WSN-Based System for Country Border Surveillance and Target Tracking. Advances in Remote Sensing,05,51-72. doi: 10.4236/ars.2016.51005