Target Localization and Tracking in Noisy Binary Sensor Networks with Known Spatial Topology

Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction‐of‐arrival (DOA) or time‐of‐arrival (TOA) information are not suitable for low‐power sensors with limited computation and communication capabilities. In this paper, we propose a low‐complexity and energy‐efficient method for target localization and tracking in noisy binary sensor networks, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise (AWGN) channels. The proposed method is based on known spatial topology. An efficient wake‐up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd.

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

Internet of Things (IoT) is constructed from billions of sensor devices connected over Internet. Wireless sensor networks (WSNs) are very important communication technologies of IoT for providing large scale data from the environment. Since the data collected grows exponentially, the design of cloud based big data analysis techniques is crucial. Localization in sensor networks is finding the location of a node based on the reference nodes’s coordinates. In many applications such as target tracking and military surveillance, providing localization is necessary. Manually entering the locations of nodes during the deployment phase is not applicable especially for large scale sensor networks. Besides, integrating a GPS receiver to each sensor node is a very costly solution and may not be affordable for large scale networks. Since sensor nodes are mostly battery-powered, design of an energy-efficient localization method is highly desirable to prolong the lifetime of the network. Existing localization techniques may require many message transmission which causes high energy consumption. To tackle with this problem, we propose an energy-efficient localization framework in this paper. A mobile robot is traversed along the sensing area and communicate with sensor nodes to localize these nodes. Different than the previous approaches, our proposed approach requires only 3 messages per node. Besides, most of the execution needed for the localization is not carried by ordinary nodes. We simulate our proposed approach in ns2 simulator. We measure the localization quality and energy consumptions of our proposed approach with its counterparts. Also, we measure the localization quality and energy consumption against varying node counts and degrees. From extensive simulation results, we obtain that the localization qualities of our proposed approach is significantly better than its competitors. Besides, the energy consumption of our proposed algorithm is just 0.06 J per node and far more better than the distributed algorithm. Conclusively, our proposed framework is a significant candidate for IoT and big data applications requiring energy-efficient localization.

In general, it requires lots of complicated and expensive processing functions to find the exact location of a mobile target. For example, Ubiquitous Robotic Companion (URC) system should be equipped with powerful resources or be given an aid from the external servers to find its location for itself. Sensor network that consists of inexpensive low-power sensors can provide an efficient solution to find the exact location of such a mobile target at a low price. In such applications, if all sensor nodes have to always wake up to find location of the mobile robot, we have to pay a lot of waste of resources such as battery power and channel utilization. In this paper, we propose a cheap and energy efficient location tracking method of a mobile robot by minimizing the number of sensor nodes participating in the task of target tracking.

Energy efficient tracking in uncertain sensor networks

Sensor networks include nodes with limited computation and communication capabilities. One of the basic functions of sensor networks is to sense and transmit data to the end users. The resource constraints and security issues pose a challenge to information aggregation in large sensor networks. Bootstrapping keys is another challenge because public key cryptosystems are unsuitable for use in resource-constrained sensor networks. In this paper, we propose a solution by dividing the problem in two domains. First, we present a protocol for establishing cluster keys in sensor networks using verifiable secret sharing. We chose elliptic curve cryptosystems for security because of their smaller key size, faster computations and reductions in processing power. Second, we develop a secure data aggregation and verification (SecureDAV) protocol that ensures that the base station never accepts faulty aggregate readings. An integrity check of the readings is done using Merkle hash trees, avoiding over-reliance on the cluster-heads.

Ad hoc Sensor Networks (ASNs) are ad-hoc mobile networks that consist of sensor nodes with limited computation and communication capabilities. Because ASNs may be deployed in hostile areas, where communication is monitored and nodes are subject to be captured by an adversary, ASNs need a cryptographic protection of communications and sensor-capture detection. According to that the ASN is deployed to carry out some certain tasks, we present a mission-guided key-management scheme. In our scheme, a key ring, which consisting of randomly chosen k keys from a sub-pool of a large offline-generated pool of P keys, is pre-distributed to each sensor node of a group. Compared with Laurent’s scheme, our scheme improves the probability that a shared key exists between two sensor nodes of the same group, and doesn’t affect its security.KeywordsAd hoc Sensor NetworksSecurityKey ManagementMission-guarded

For implementing dynamic target tracking, two distributed CCD (charge-coupled device) cameras are set up to capture the poses of the tracking and target cars, which have the front-wheel for the steering orientation and the rear-wheel for the forward-backward motion. Based on the control authority of these two CCD cameras, a suitable reference command for the proposed controller of the tracking car is planned on a personal computer and then transmitted to the tracking car by a wireless device. Only the information of the upper bound of system knowledge is required to select the suitable scaling factors and coefficients of sliding surface for the proposed controller so that an acceptable performance is achieved. Since the target car is dynamic and the tracking car possesses dynamics, a grey prediction for the pose of the target car is employed to plan an effective reference command for the tracking car to enhance the performance of target tracking. Finally, a sequence of experiments is carried out to confirm the usefulness of the proposed control system.

The purpose of the work in this paper is to gain insight into several fundamental questions that arise whenever a distributed multiple-vehicle control system is endowed with communication capabilities. These fundamental questions include: what data should each vehicle share?, how frequently should communication take place?, and what benefit does communication provide? These questions are evaluated with respect to a target tracking task in which multiple pursuit vehicles estimate the state of a target vehicle. This task has three main components: communication, estimation, and control. Communication takes place on a broadcast network, estimation is achieved with an Unscented Kalman Filters, and the controller is behavior-based. Simulation results show that communication always improves distributed estimate results. Which information to transmit depends on available bandwidth, and more frequent communication generally yields better estimates. Simulation results also show how coordinated control can be beneficial to target tracking in a cluttered environment.

A novel energy efficient target tracking approach is proposed for wireless multimedia sensor networks: ARMA and piecewise Cubic Spline interpolation based Adaptive Sampling model (ACSAS). The least square based acoustic signal energy ratio localization model is presented. With unequal interval historical target positions interpolated by piecewise cubic spline interpolation, the target position is forecasted by ARMA. Sampling interval is dynamically determined and updated based on target future location and velocity. Sensor nodes near the forecasted position are awakened at the next sampling. Compared with Non-ACSAS, the simulation results have verified that ACSAS greatly reduces the tracking energy consumption of WMSN for its much lower computational cost.KeywordsWireless multimedia sensor networksenergy efficiencytarget trackingadaptive samplingARMApiecewise cubic spline

This paper addresses the challenge of reduced tracking accuracy in maritime electro-optical tracking equipment when dealing with high-mobility targets like speedboats and aircraft due to off-target error delays. We propose an innovative technique that leverages inertial navigation data to enhance target prediction and tracking control. Our approach involves the real-time integration of high-frequency inertial navigation-derived attitude information into the tracking system. By combining off-target error information with angular measurements from the tracking mechanism, we project the vector of the tracked target into multiple coordinate systems, including the imaging coordinate system, carrier coordinate system, and geographic coordinate system. Subsequently, we model and predict the target’s motion trajectory in the relatively slow-changing geographic coordinate system. This transformation process increases the update frequency and real-time performance of the tracking control position loop command angle. Unlike traditional control methods that heavily rely on the model of the controlled object, our approach significantly improves tracking accuracy and engineering applicability. It offers a technology-based optimization of tracking and control performance through an interdisciplinary theoretical fusion, deeply integrating inertial navigation technology with tracking control technology. Experimental results with maritime electro-optical tracking equipment demonstrate that our proposed control technique increases tracking accuracy for high-speed targets by approximately threefold compared to traditional methods. Under the same experimental conditions, the off-target error statistics are reduced from 1.8 mrad to 633 μrad.

We propose a two stage beamforming technique for localisation of moving target in a passive radar environment. In order to perform localization and tracking in Cartesian plane, in addition to detection in the range Doppler domain, it is necessary to have angle of arrival of target return, which is obtained using beamforming. The beamforming technique can also enhance signal to disturbance ratio. However, performing constant false alarm rate detection (CFAR) in this case requires storing beamformer output statistics for every range-Doppler-azimuth bin which significantly burdens the system and hinders the real time processing and tracking of moving targets. Using experimental measurements, we demonstrate real time detection and tracking of an airborne target using a two stage beamforming approach, where a smaller set of beams is used in the first stage for CFAR detection and a larger set of beams is used in the second stage for angle of arrival estimation with higher accuracy.

Wireless Sensor Network (WSN) consists of small battery-powered sensor nodes with limited energy resources, with sensing, computation, and communications capabilities. One of the crucial issues in WSN is energy consumption thus, poor energy efficient routing. The lifetime of the entire network depending on the energy efficiency of the sensor network which finally requires an energy- efficient routing protocol. This paper provides energy-efficient routing by using a fuzzy logic approach in the clusterhead selection process that provides a completely non-probabilistic approach in order to balance the energy consumption of nodes and prolong the WSN lifetime. This approach uses three fuzzy variables: Residual energy of the nodes, distance to Base station and distance to the cluster-head. Simulations results show that the proposed algorithm improves energy consumption and significantly prolong the network lifetime in wireless sensor networks when compared with LEACH protocol.

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments.

: Optimizing sensor networks involves addressing a wide range of issues. These issues stem from limited energy reserves, computation power, communication capabilities, and self-managing sensor nodes. The NS-2 simulation environment is a flexible tool for network engineers to investigate how various protocols perform with different configurations and topologies. This report describes how we extended the NS-2 framework to include support for sensor networks.