Range-based Algorithm Research Articles

Robust Localization System Using Vector Combination in Wireless Sensor Networks

This paper proposes a vector-based localization system that uses both distance and angle information. In wireless sensor networks, the positions of nodes are commonly determined by a range-based localization system using distance information. If both distance and angle information are available, it is possible to improve the accuracy of estimating the positions of nodes compared to a positioning system with only distance information. Existing studies using distance and angle information assume that all the nodes are directly connected to one another and do not consider a method for measuring angle information between the nodes that are not directly connected. However, this assumption may not be valid for realworld wireless sensor networks especially with a large number of nodes having a limited communication range. The proposed localization algorithm solves this problem by a vector combination that transforms the vectors on the local coordinate system to the network-wide global coordinate system. The proposed algorithm is shown to be robust especially even in a network with 1-edge connectivity. Simulation results show that the proposed algorithm has up to 70% higher positioning accuracy compared to the existing iterative range-based algorithm such as MDS-MAP(C,R).

A Range-Based Algorithm for Autonomous Navigation of an Aerial Drone to Approach and Follow a Herd of Cattle.

This paper proposes an algorithm that will allow an autonomous aerial drone to approach and follow a steady or moving herd of cattle using only range measurements. The algorithm is also insensitive to the complexity of the herd’s movement and the measurement noise. Once arrived at the herd of cattle, the aerial drone can follow it to a desired destination. The primary motivation for the development of this algorithm is to use simple, inexpensive and robust sensing hence range sensors. The algorithm does not depend on the accuracy of the range measurements, rather the rate of change of range measurements. The proposed method is based on sliding mode control which provides robustness. A mathematical analysis, simulations and experimental results with a real aerial drone are presented to demonstrate the effectiveness of the proposed method.

Analysis and experimental comparison of range-based control for dual-stage nanopositioners

In this paper, a novel range-based control algorithm for dual-stage nanopositioners is studied. Dual-stage nanopositioning systems have the powerful ability to achieve high-speed and long-range positioning by coupling a short-range, high-speed actuator with a long-range, low-speed actuator. One of the drawbacks of currently implemented control algorithms is that they tend to determine control allocation to the individual actuators based on the frequency of the desired motion. This can result in reduced positioning resolution and increased energy use, thus motivating this work on a control algorithm that considers range. The range-based algorithm allows the user to allocate control efforts to the individual actuators based on their range capabilities. This paper discusses the analysis, design, and implementation of the range-based control algorithm and highlights its benefits by experimentally comparing it to four other widely used dual-stage positioner control algorithms. Experimental results show that the method effectively splits control effort between the actuators based on range and results in reduced tracking error.

Flood Control Distance Vector-Hop (FCDV-Hop) Localization in Wireless Sensor Networks

Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. Anchor nodes generate the packets of nodes position information to calculate the number of hops between the beacon nodes and the anchor nodes, and these packets are flooded in the respective Wireless Sensor Networks (WSN). The DV-Hop algorithm obtains the distance information from the unknown nodes to the anchor nodes through the network topology calculation rather than the radio wave signals measurement which are used in the range-based algorithm. However, flooding information from all anchors to all beacon nodes will become too expensive for large networks (i.e., cost, energy), even with low anchor fraction. The region of nodes flooding through the network needs to be confined within logical constraints to produce better accuracy to reduce the localization error while reducing the cost. This research proposes and develops an enhanced algorithm that can improve the region area for sensor nodes placement within the specific localization area. This research aims to produce a longer network lifetime by reducing the energy used of the sensor nodes and at the same time minimize the localization error. This research describes the flooding control of the node's placement for the localization of the DV-Hop algorithm. The placement of nodes is limited inside the region of the anchor nodes. The simulation environment setup comes with parameters such as the radius, area, number of nodes, types of nodes, the anchor proportion, and the value of the regions. The result shows an improvement for the localization error, the power consumption, and the accuracy of the nodes positioning.

ARBSA: Adaptive Range-Based Simulated Annealing for FPGA Placement

Placement has always been the most time-consuming part of the field programmable gate array (FPGA) compilation flow. Conventional simulated annealing has been unable to keep pace with ever increasing sizes of designs and FPGA chip resources. Without utilizing information of the circuit topology, it relies on large amounts of random swap operations, which are time-costly. This paper proposes an adaptive range-based algorithm to improve the behavior of swap operations and limit the swap distances by introducing the concept of range-limiting strategy for nets. It avoids unnecessary design space exploration, and thus can converge to near-optimal solutions much more quickly. The experimental results are based on the Titan benchmarks, which contain 4K to 30K blocks, including logic array blocks, inputs and outputs, digital signal processors, and random access memories. This approach achieves $2.82\boldsymbol \times $ speed up, 4.8% reduction on wire length, 4.1% improvement on critical path compared with the SA from VTR with wire length-driven optimization, and $1.78\boldsymbol \times $ speed up, 10% reduction on wire length, 2% reduction on critical path with path timing-driven optimization. It also manifests better scalability on larger benchmarks.

Localization and Detection of Targets in Underwater Wireless Sensor Using Distance and Angle Based Algorithms

Underwater localization is used as a key element in most applications of underwater communications. Despite the global positioning system (GPS) receivers are usually employed in terrestrial wireless sensor networks, they cannot be exploited for underwater localization. In fact, GPS signals are highly attenuated by the water, by being unusable to a depth of more than a couple of meters, and cannot propagate underwater, especially, in the case of the salt water. In place of RF signals, acoustic signals are the most common mode of communication, and the so-called underwater acoustic sensor networks attracted a significant interest due to their great impact on ocean monitoring and exploration. Hydroacoustics, as the study and application of sound in water, is the foundation of underwater localization, but the existing available methods, classified in range-based versus range-free techniques, are affected by several open problems and research challenges. Therefore, an accurate range-based algorithm for localization needs to be developed, and the demand for expeditiously employing the energy of the sensor nodes is still remaining a distinct feature for underwater wireless sensor networks. Because of these argues, an improved interpretation for underwater localization is presented, by first presenting a general localization algorithm, and afterward deploying the ordinary, beacon nodes in order to find the error and accuracy of sensor localization. After that, we present two localization algorithms named as distance-based and angle-based algorithms. We consider a realistic case, where sensor nodes are not time synchronized and the sound speed in water is unknown. The simulation results exhibit that our algorithms compensate for time synchronization, estimate the mean errors in localization and achieve good localization accuracy.

Multi-targets localization based on ARMA and GA in WSNs

Multi-target localization methods for locating of the movingtarget in interested area monitored by Wireless Sensor Networks (WSNs) are nowadays a popular subject of study. The methods can be classified into two categories: range-free algorithm and range-based algorithm. In this work, we propose a novel multi-target localization method, which belongs to the category of range-based algorithm, by using a genetic algorithm (GA) for searching optimal solution of the objective function of multi-target localization. The objective function is only a group of linear equations with independent variables of acoustic energies calculated at each sensor-node in a WSN. However, application of the method, the accuracy of multi-target localization is sensitive to the SNR of the measured sound signals at each node, thus a denoising strategy should be inserted into the method. It turned out that the measured sound noise, comparing intrinsic sensor noise and environmental noise, may be considered as an Autoregressive Moving Average (ARMA) process. Thus, by building the ARMA model, the noise sequence commingled with the target signals can be predicted. As a consequence, the power of the noises can be subtracted from the measured sound signals for revealing the target signal's power. The results in present work demonstrate the advantage of the proposed method.

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme.

Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes.

Cooperative method for wireless sensor network localization

In order to obtain an efficient wireless sensor network localization, several enhancements based on the decentralized approach are proposed. These features can be used in the cases when multiple distance measurements are used as input, where each node iteratively updates its estimated position using a maximum likelihood estimation method based on the previously estimated positions of its neighbors. Three novel features are introduced. First, a backbone is constructed, that is, a subset of nodes that are intermediaries between multiple beacon nodes, which guides the localization process of the other (non-backbone) nodes. Second, the space is perturbed more often during the earlier time steps to avoid reaching poor local minima in some cases regarding the localization optimization function. Third, for better localization of the non-backbone (or peripheral) nodes and avoidance of the rigidity problem, 2-hop neighboring distances are approximated. The introduced features are incorporated in a range-based algorithm that is fully distributed, shows good performance, and is scalable to arbitrary network size.

Handling data skew in join algorithms using MapReduce

One of the major obstacles hindering effective join processing on MapReduce is data skew. Since MapReduce’s basic hash-based partitioning method cannot solve the problem properly, two alternatives have been proposed: range-based and randomized methods. However, they still remain some drawbacks: the range-based method does not handle join product skew, and the randomized method performs worse than the basic hash-based partitioning when input relations are not skewed. In this paper, we present a new skew handling method, called multi-dimensional range partitioning (MDRP). The proposed method overcomes the limitations of traditional algorithms in two ways: 1) the number of output records expected at each machine is considered, which leads to better handling of join product skew, and 2) a small number of input records are sampled before the actual join begins so that an efficient execution plan considering the degree of data skew can be created. As a result, in a scalar skew experiment, the proposed join algorithm is about 6.76 times faster than the range-based algorithm when join product skew exists and about 5.14 times than the randomized algorithm when input relations are not skewed. Moreover, through the worst-case analysis, we show that the input and the output imbalances are less than or equal to 2. The proposed algorithm does not require any modification to the original MapReduce environment and is applicable to complex join operations such as theta-joins and multi-way joins.

Research on range-based localisation algorithm with virtual force and time synchronisation in wireless sensor and actor networks

In order to solve the problem of sensor node localisation of wireless sensor and actor networks, a range-based time of arrival TOA localisation algorithm with virtual force and time synchronisation TLAVFTS in WSAN is proposed in this paper, which uses mobile actors instead of WSNs anchors, and combines TOA with virtual force on the premise of time synchronisation. When a sensor node requests for localisation, the algorithm first calculates the virtual force of actor nodes, and makes it move close to the requested sensor node, then synchronise time of entire network. Finally, three-bonier measurement method will be adopted to complete node localisation. The simulation results show that the proposed algorithm can improve the success localisation rate by up to 40% and also is superior to traditional TOA on the average localisation time, error and overhead; it can apply to real-time filed with small number of actor nodes.

1. Closed-Loop Control with DIAS vs. Sensor-Augmented Pump Therapy in Adolescents and Young Adults with Type 1 Diabetes at Camp (99-L)

The DIAS system utilizes a control to range-based algorithm for glucose control in type 1 diabetes. The system includes a Dexcom G4 sensor, a Roche AccuCheck pump and an Android-based DIAS controller. The objective of this study was to test the safety and efficacy of DIAS in subjects with type 1 diabetes, aged 10-35 y, over 5 days at diabetes camp. There were 33 subjects randomized to either closed-loop with DIAS (intervention) or sensor-augmented pump (control) for 5 days at diabetes camp. Both groups utilized premeal boluses.

Multiperiod robust optimization for proactive resource provisioning in virtualized data centers

Energy management has become a significant concern in data centers for reducing operational costs. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off idle servers. This needs to consider the power state change overhead. In this paper, we investigate proactive resource provisioning in short-term planning for performance and energy management. To implement short-term planning based on workload prediction, this requires dealing with high fluctuations that are inaccurately predictable by using single value prediction. Unlike long-term planning, short-term planning can not depend on periodical patterns. Thus, we propose an adaptive range-based prediction algorithm instead of a single value. We implement and extensively evaluate the proposed range-based prediction algorithm with different days of real workload. Then, we exploit the range prediction for implementing proactive provisioning using robust optimization taking into consideration uncertainty of the demand. We formulate proactive VM provisioning as a multiperiod robust optimization problem. To evaluate the proposed approach, we use several experimental setups and different days of real workload. We use two metrics: energy savings and robustness for ranking the efficiency of different scenarios. Our approach mitigates undesirable changes in the power state of servers. This enhances servers' availability for accommodating new VMs, its robustness against uncertainty in workload change, and its reliability against a system failure due to frequent power state changes.

Range-Based Localization in Wireless Networks Using Density-Based Outlier Detection

Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.

ROBNAV: A Range-Based Robot Navigation and Obstacle Avoidance Algorithm

An algorithm has been developed that efficiently solves a large class of robot navigation and obstacle avoidance problems using range information as its sole input from the environment. The system resides in a minicomputer and requires very small memory (1500 words) and computing time (1.35 s) allocations while solving simulated problems of broadly ranging spatial complexity and operational intricacy. It is thus a prime candidate for use in mobile robots or manipulators where real-time operation is desired.