Object Tracking In Sensor Networks Research Articles

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

The development of pervasive communication devices and the emergence of the Internet of Things (IoT) have acted as an essential part in the feasibility of smart city initiatives. Wireless sensor network (WSN) as a key enabling technology in IoT offers the potential for cities to get smarter. WSNs gained tremendous attention during the recent years because of their rising number of applications that enables remote monitoring and tracking in smart cities. One of the most exciting applications of WSNs in smart cities is detection, monitoring, and tracking which is referred to as object tracking sensor networks (OTSN). The adaptation of OTSN into urban cities brought new exciting challenges for reaching the goal of future smart cities. Such challenges focus primarily on problems related to active monitoring and tracking in smart cities. In this paper, we present the essential characteristics of OTSN, monitoring and tracking application used with the content of smart city. Moreover, we discussed the taxonomy of OTSN along with analysis and comparison. Furthermore, research challenges are investigated concerning energy reservation, object detection, object speed, accuracy in tracking, sensor node collaboration, data aggregation and object recovery position estimation. This review can serve as a benchmark for researchers for future development of smart cities in the context of OTSN. Lastly, we provide future research direction.

Robust scheduling of wireless sensor networks for target tracking under uncertainty

An object tracking sensor network (OTSN) is a wireless sensor network designed to track moving objects in its sensing area. It is made of static sensors deployed in a region for tracking moving targets. Usually, these sensors are equipped of a sensing unit and a non-rechargeable battery. The investigated mission involves a moving target with a known trajectory, such as a train on a railway or a plane in an airline route. In order to save energy, the target must be monitored by exactly one sensor at any time. In our context, the sensors may be not accessible during the mission and the target can be subject to earliness or tardiness. Therefore, our aim is to build a static schedule of sensing activities that resists to these perturbations. A pseudo-polynomial two-step algorithm is proposed. First, a discretization step processes the input data, and a mathematical formulation of the scheduling problem is proposed. Then, a dichotomy approach that solves a transportation problem at every iteration is introduced; the very last step is addressed by solving a linear program.

Distributed object tracking using moving trajectories in wireless sensor networks

Most recent research on object tracking sensor networks has focused on collecting all data from the sensor network into the sink, which delivers the predicted locations to the corresponding nodes in order to accurately predict object movement. The communication cost of this centralized scenario is higher than that of a distributed method. Centralized data collection affects the freshness of the data and increases latency in movement trajectory prediction. In addition, due to the large amount of packets being sent and received, sensor node energy is quickly exhausted. Although this data collection method might result in higher accuracy for prediction, the sensor network lifetime is not reduced. In this paper, a distributed object tracking method is proposed using the network structure of convex polygons, called faces. The nodes in the faces cooperate to find the trajectories of an object and then these trajectories are used to predict the objects' movement. The proposed method, based on trajectory tree construction, can reduce both the storage space of collected trajectories and the time spent on trajectory prediction analysis. Simulations show that the proposed method can reduce the energy consumption of the nodes and make prediction of nodes moving direction accurately than the existing approaches.

A hybrid scheme for energy-efficient object tracking in sensor networks

Energy saving is a critical issue in many sensor-network-based applications. Among the existing sensor-network-based applications, the surveillance application has attracted extensive attention. Object tracking in sensor networks (OTSNs) is a typical surveillance application. Previous studies on energy saving for OTSNs can be divided into two main approaches: (1) improvements in hardware design to lower the energy consumption of attached components and (2) improvements in software to predict the movement of objects. In this paper, we propose a novel scheme, namely hybrid tracking scheme (HTS), for tracking objects with energy efficiency. The scheme consists of the two parts: (1) adaptive schedule monitoring and (2) a recovery mechanism integrated with seamless temporal movement patterns and seeding-based flooding to relocate missing objects with the purpose of saving energy. Furthermore, we also propose a frequently visited periods mining algorithm, which discovers the corresponding frequently visited periods for adaptive schedule monitoring efficiently from the visitation information of sensor nodes. To decrease the number of sensor nodes activated in flooding, a seeding-based flooding mechanism is first proposed in our work. Empirical evaluations of various simulation conditions and real datasets show that the proposed HTS delivers excellent performance in terms of energy efficiency and low missing rates.

Crisis management using MAS-based wireless sensor networks

Wireless communication is increasingly used to manage large-scale crises (e.g., natural disasters or a large-scale city fire). Communication has traditionally been based on cellular networks. However, real-life experience has proven that the base stations of these networks may collapse or become unreachable during a crisis. An incident commander must also know as much information as possible about the occurring events to control them quickly and efficiently. This paper thus proposes a crisis management approach that overcomes the problems encountered by the base stations and insures relevant, rich and real-time information about events. This approach is based on wireless sensor networks, which are distributed in nature with no need for infrastructure and could be deployed in dangerous and inaccessible zones to gather information. Our proposal uses a multi-agent system as a software layer. The multi-agent system aims to improve the wireless sensor network performance by allowing cooperation between sensor nodes, offering better lifetime management and virtualizing the application layer. This virtualization supports several required applications simultaneously, including event monitoring and object tracking. Through successive simulations, we prove the importance of our approach in crisis management using several criteria to estimate the position’s error in object tracking, end-to-end delay and wireless sensor network lifetime management.

Enable Energy Efficiency of Object Tracking using Sentinel based Multisensor Sleep Scheduling

As a way of lifetime extension, the energy efficiency has become a crucial criterion in the design of object tracking sensor networks (OTSN). In this paper, we propose a sentinel based sleep scheduling called Sleep Scheduling Protocol (SSP) to reduce the number of the awakened sensor nodes with less performance loss while existing solutions either suffer from increasing of event detecting delays or computing overhead. SSP is built upon cluster based network structure. Each cluster contains group of sensor nodes divided in terms of geometric information. Sentinels will be selected from each cluster for probing object moving trajectory and maintaining communication routes. In the approach, sentinels will remain active or be scheduled to periodic sleep. The rest of the nodes remain sleeping until being waked up by sentinels and only maintain low communication power to stay accessible for energy saving. We compare SSP with three sleep based scheduling protocols via simulation. The results show that SSP can reduce average energy consumption but with reasonable detection probability and delay.

저밀도 센서 네트워크 환경에서 다항 회귀 예측 기반 이동 객체 추적 기법

무선 센서 네트워크 환경에서 이동 객체 추적 기법은 환경 모니터링이나 군사 지역에서 적의 이동을 추적하는 실제 응용을 위한 핵심적인 기반 기술이다. 기존 연구에서는 저밀도를 갖는 실제 센서 네트워크 환경에 의해 발생되는 감지 공백 영역을 고려하지 않았다. 따라서 많은 이동 객체 추적 실패가 발생하여 에너지 소모가 증가하였다. 이러한 문제를 해결하기 위해 본 논문에서는 저밀도 환경과 감지 공백 영역을 고려한 이동 객체 추적 기법을 제안한다. 제안하는 기법은 다항 회귀 분석을 이용해 객체의 경로를 예측하여 최소한의 센서 노드를 활성화시킨다. 또한 이동 객체 추적 실패가 발생할 경우 감지 공백 영역의 경계 노드만을 활성화 시키는 객체 추적 복구 기법을 수행한다. 이를 통해, 제안하는 기법은 에너지 소모량을 줄이고 감지 공백 영역 안에서도 높은 예측 정확도를 보장한다. 제안하는 기법이 기존 기법에 비해 이동 객체 추적에 소모되는 에너지를 평균 약 47% 감소시켰고, 센서 노드가 낮은 밀도로 배치된 상황에서 발생하는 감지 공백 영역에서도 평균 약 91%의 예측 정확도를 보였다. In wireless sensor networks, a moving object tracking scheme is one of core technologies for real applications such as environment monitering and enemy moving tracking in military areas. However, no works have been carried out on processing the failure of object tracking in sparse sensor networks with holes. Therefore, the energy consumption in the existing schemes significantly increases due to plenty of failures of moving object tracking. To overcome this problem, we propose a novel moving object tracking scheme based on polynomial regression prediction in sparse sensor networks. The proposed scheme activates the minimum sensor nodes by predicting the trajectory of an object based on polynomial regression analysis. Moreover, in the case of the failure of moving object tracking, it just activates only the boundary nodes of a hole for failure recovery. By doing so, the proposed scheme reduces the energy consumption and ensures the high accuracy for object tracking in the sensor network with holes. To show the superiority of our proposed scheme, we compare it with the existing scheme. Our experimental results show that our proposed scheme reduces about 47% energy consumption for object tracking over the existing scheme and achieves about 91% accuracy of object tracking even in sensor networks with holes.

Energy-efficient data reporting with sink mobility support in object tracking sensor networks

This study proposes an energy–efficient data reporting scheme that supports sink mobility in Object Tracking Sensor Networks (OTSN). To achieve energy efficiency, the proposed scheme presents an active node selection and scheduling method to save and balance the energy consumption among nodes based on a virtual grid structure. In addition, the proposed scheme develops an elegant weight function associated with active nodes, allowing active nodes to forward data to the mobile sink efficiently. This study also proposes an object tracking method. Simulation results show that the proposed scheme outperforms TNT and SNEM in terms of the energy consumption and average path length.

Survey of mobile object tracking protocols in wireless sensor networks: a network-centric perspective

Mobile object tracking in Wireless Sensor Networks (WSNs) has gained much attention during recent years owing to its growing application potential for the ubiquitous society. In this paper, we present an investigation and taxonomy of the state-of-the-art in tracking moving objects in WSNs. To this end, we define the target tracking problem in general and introduce the major design considerations for an efficient Object Tracking Sensor Network (OTSN) in particular. We typify a complete object tracking solution as a system of signal processing-based algorithms in charge of target identification/classification and network-centric protocols as the communication substrate. Our special highlight would be on the discussion of the network-centric protocols built around the notion of OTSN. We outline the advantages as well as the performance issues in relevant schemes and conduct a brief comparative study. The survey concludes with several key open problems together with a summary of directions for future works.

A Predictive Energy-Efficient Technique to Support Object-Tracking Sensor Networks

In recent years, we have witnessed an increasing interest in deploying wireless sensor networks (WSNs) for real-life applications. However, before WSNs become a commodity, several challenging issues remain to be resolved. Object-tracking sensor network (OTSN)-based applications are widely viewed as being among the most interesting applications of WSNs. OTSN is mainly used to track certain objects in a monitored area and to report their location to the application's users. However, OTSNs are well known for their energy consumption when compared with other WSN applications. In this paper, we propose a prediction-based tracking technique using sequential patterns (PTSPs) designed to achieve significant reductions in the energy dissipated by the OTSNs while maintaining acceptable missing rate levels. PTSP is tested against basic tracking techniques to determine the appropriateness of PTSP under various circumstances. Our experimental results have shown that PTSP outperforms all the other basic tracking techniques and exhibits significant amounts of savings in terms of the entire network's energy consumption.

A Prediction-based Energy-conserving Approximate Storage and Query Processing Schema in Object-Tracking Sensor Networks

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. In object-tracking sensor networks, the data storage and query processing should be energy-conserving by decreasing the message complexity. In this paper, a Prediction-based Energy-conserving Approximate StoragE schema (P-EASE) is proposed, which can reduce the query error of EASE by changing its approximate area and adopting predicting model without increasing the cost. In addition, focusing on reducing the unnecessary querying messages, P-EASE enables an optimal query algorithm to taking into consideration to query the proper storage node, i.e., the nearer storage node of the centric storage node and local storage node. The theoretical analysis illuminates the correctness and efficiency of the P-EASE. Simulation experiments are conducted under semi-random walk and random waypoint mobility. Compared to EASE, P-EASE performs better at the query error, message complexity, total energy consumption and hotspot energy consumption. Results have shown that P-EASE is more energy-conserving and has higher location precision than EASE.

A global profile-based algorithm for energy minimization in object tracking sensor networks

Recent progress in sensing devices has made wireless sensor networks (WSNs) possible. One of their most prominent applications is object tracking. Scarce battery resources available in the sensor nodes raise the problem of energy consumption minimization in WSNs. In this paper, we exploit long term patterns in the objects’ movements to reduce the network’s energy utilization. Once the nodes have learned the objects’ movement patterns, their future location is predicted and only those nodes in the network that are expected to host the objects are woken up to detect and track them. We model the problem in an analytical way and formulate the cost of tracking the object during an application’s tracking period. We also perform extensive simulations that confirm the significant improvement brought by our proposed scheme as compared to other existing approaches.

A novel prediction-based strategy for object tracking in sensor networks by mining seamless temporal movement patterns

Energy saving in sensor networks has received a great deal of research attention in recent years due to its wide applications. One important research issue is energy efficient object tracking in sensor networks (OTSNs). Past studies on energy saving in OTSNs can be divided into two main directions: (1) improvement in hardware design; and (2) improvement in software approaches. Many research papers save energy in hardware design, but few discuss software approaches. The intuitive way to conserve the energy of sensor nodes is to reduce the operation time of high-powered components. Utilizing the movement patterns of objects to save energy is one software approach. However, it did not take temporal information into consideration nor did it define a suitable segmenting time unit of time interval in advance. Due to the time interval between movements is a real number, an improper segmenting time unit may not discover the useful patterns, directly resulting in the inefficient object tracking. In this paper, we propose a seamless data mining algorithm named STMP-Mine to efficiently discover the temporal movement patterns of objects in sensor networks without predefining the segmenting time unit. Moreover, we propose novel location prediction strategies that employ the discovered temporal movement patterns to reduce prediction errors to save energy. With empirical evaluation on simulated data, STMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability and energy efficiency.

Imprecision-Tolerant Location Management for Object-Tracking Wireless Sensor Network

An important issue of wireless sensor networks is object tracking, where the key steps include event detection, target classification, location estimation and location management. The main theme of this paper is location management. Because imprecision is an inherent property in object-tracking sensor networks, this paper focuses on the scenarios where users can tolerate a certain degree of imprecision in their query results. We intend to develop a location management scheme that can achieve two goals. First, multiple precision levels are provided. Second, the query cost is proportional to the precision level. To achieve these two goals, we propose a tree-based imprecision-tolerant location management scheme that includes three major components: (1) update and query mechanisms that can support imprecision-tolerant queries, (2) the approach to taking the statistics of imprecision-tolerant queries and (3) a tree construction algorithm that can reduce the query cost and minimize the increment of update cost. Performance evaluations are conducted through simulations to verify the proposed scheme.

A Scheme of Restricted Flooding and Greedy Forwarding Routing for Object Tracking Sensor Networks

A protocol named RFGFR (Restricted Flooding and Greedy Forwarding Routing) is presented in this paper. RFGFR propagates query packets from the sink to the centroid of a target region, it employs a scheme of restricted flooding and greedy forwarding results in fewer nodes to forward packets, which means less energy consumption. Greedy forwarding decision can reduce transmission delay by reducing communication hops. Simulations show RFGFR exhibits better performances for object tracking in sensor networks.

Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns

A number of studies have been written on sensor networks in the past few years due to their wide range of potential applications. Object tracking is an important topic in sensor networks; and the limited power of sensor nodes presents numerous challenges to researchers. Previous studies of energy conservation in sensor networks have considered object movement behavior to be random. However, in some applications, the movement behavior of an object is often based on certain underlying events instead of randomness completely. Moreover, few studies have considered the real-time issue in addition to the energy saving problem for object tracking in sensor networks. In this paper, we propose a novel strategy named multi-level object tracking strategy (MLOT) for energy-efficient and real-time tracking of the moving objects in sensor networks by mining the movement log. In MLOT, we first conduct hierarchical clustering to form a hierarchical model of the sensor nodes. Second, the movement logs of the moving objects are analyzed by a data mining algorithm to obtain the movement patterns, which are then used to predict the next position of a moving object. We use the multi-level structure to represent the hierarchical relations among sensor nodes so as to achieve the goal of keeping track of moving objects in a real-time manner. Through experimental evaluation of various simulated conditions, the proposed method is shown to deliver excellent performance in terms of both energy efficiency and timeliness.

A New Storage Scheme for Approximate Location Queries in Object-Tracking Sensor Networks

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. Observing that many sensor applications for object tracking can tolerate a certain degree of imprecision in the location data of tracked objects, this paper studies precision-constrained approximate queries that trade answer precision for energy efficiency. We develop an energy-conserving approximate storage (EASE) scheme to efficiently answer approximate location queries by keeping error-bounded imprecise location data at some designated storage node. The data impreciseness is captured by a system parameter called the approximation radius. We derive the optimal setting of the approximation radius for our storage scheme based on the mobility pattern and devise an adaptive algorithm to adjust the setting when the mobility pattern is not available a priori or is dynamically changing. Simulation experiments are conducted to validate our theoretical analysis of the optimal approximation setting. The simulation results show that the proposed EASE scheme reduces the network traffic from a conventional approach by up to 96 percent and, in most cases, prolongs the network lifetime by a factor of 2-5.

Localized monitoring of kNN queries in wireless sensor networks

Wireless sensor networks have been widely used in civilian and military applications. Primarily designed for monitoring purposes, many sensor applications require continuous collection and processing of sensed data. Due to the limited power supply for sensor nodes, energy efficiency is a major performance concern in query processing. In this paper, we focus on continuous kNN query processing in object tracking sensor networks. We propose a localized scheme to monitor nearest neighbors to a query point. The key idea is to establish a monitoring area for each query so that only the updates relevant to the query are collected. The monitoring area is set up when the kNN query is initially evaluated and is expanded and shrunk on the fly upon object movement. We analyze the optimal maintenance of the monitoring area and develop an adaptive algorithm to dynamically decide when to shrink the monitoring area. Experimental results show that establishing a monitoring area for continuous kNN query processing greatly reduces energy consumption and prolongs network lifetime.

Energy efficient strategies for object tracking in sensor networks: A data mining approach

In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) due to the wide applications. One important research issue in OTSNs is the energy saving strategy in considering the limited power of sensor nodes. The past studies on energy saving in OTSNs considered the object’s movement behavior as randomness. In some real applications, however, the object movement behavior is often based on certain underlying events instead of randomness completely. In this paper, we propose a novel data mining algorithm named TMP-Mine with a special data structure named TMP-Tree for efficiently discovering the temporal movement patterns of objects in sensor networks. To our best knowledge, this is the first work on mining the movement patterns associated with time intervals in OTSNs. Moreover, we propose novel location prediction strategies that utilize the discovered temporal movement patterns so as to reduce the prediction errors for energy savings. Through empirical evaluation on various simulation conditions and real dataset, TMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.