Begumhan Turgut

Survey Paper: Routing protocols in ad hoc networks: A survey

Ad hoc wireless networks perform the difficult task of multi-hop communication in an environment without a dedicated infrastructure, with mobile nodes and changing network topology. Different deployments exhibit various constraints, such as energy limitations, opportunities, such as the knowledge of the physical location of the nodes in certain scenarios, and requirements, such as real-time or multi-cast communication. In the last 15years, the wireless networking community designed hundreds of new routing protocols targeting the various scenarios of this design space. The objective of this paper is to create a taxonomy of the ad hoc routing protocols, and to survey and compare representative examples for each class of protocols. We strive to uncover the requirements considered by the different protocols, the resource limitations under which they operate, and the design decisions made by the authors.

Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach

We show how genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. In particular, we optimize our recently proposed weighted clustering algorithm (WCA). The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique. Encoding the individual chromosomes is an essential part of the mapping process; each chromosome contains information about the clusterheads and the members thereof, as obtained from the original WCA. The genetic algorithm then uses this information to obtain the best solution (chromosome) defined by the fitness function. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the medium access control (MAC) protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA. Moreover, the loads among clusters are more evenly balanced by a factor of ten.

Optimizing clustering algorithm in mobile ad hoc networks using simulated annealing

In this paper, we demonstrate how simulated annealing algorithm can be applied to clustering algorithms used in ad hoc networks; specifically our recently proposed weighted clustering algorithm (WCA) is optimized by simulated annealing. As the simulated annealing stands to be a powerful stochastic search method, its usage for combinatorial optimization problems was found to be applicable in our problem domain. The problem formulation along with the parameters is mapped to be an individual solution as an input to the simulated annealing algorithm. Input consists of a random set of clusterhead set along with its members and the set of all possible dominant sets chosen from a given network of N nodes as obtained from the original WCA. Simulated annealing uses this information to find the best solution defined by computing the objective function and obtaining the best fitness value. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the MAC protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA.

Restarting Particle Filters: An Approach to Improve the Performance of Dynamic Indoor Localization

Particle filters have been found to be effective in tracking mobile targets in indoor environments. One frequently encountered problem in these settings occurs when the targets movement pattern changes unexpectedly; such as when the target turns around, enters a room from a corridor or turns left or right at an intersection. If the particle filter makes an incorrect prediction, it might not be able to recover using the normal techniques of prediction, weight update and resampling. We propose an approach to automatically restart the particle filter by sampling the latest trusted observation when the particle cloud diverges too much from the observations. The restart decision is based on Kullback-Leibler divergence between the probability surfaces associated with the current observation and the particle cloud. Through an experimental study we show that the restart algorithm allows the successful early recovery of stranded particle filters, in our scenarios providing a 36% average improvement in localization accuracy.

GRAIL: a general purpose localization system

Purpose – The purpose of this paper is to describe a general purpose localization system, GRAIL. GRAIL provides real‐time, adaptable, indoor localization for wireless devices.Design/methodology/approach – In order to localize as diverse a set of devices as possible, GRAIL utilizes a centralized, anchor‐based approach. GRAIL defines an abstract data model for various system components to support different physical modalities. The scalable architecture of GRAIL provides maximum flexibility to integrate various localization algorithms.Findings – The authors show through real deployments that GRAIL functions over a variety of physical modalities, networks, and algorithms. Further, the authors found that a centralized solution has critical advantages over distributed implementations for handling privacy concerns.Originality/value – A key contribution of this system is its universal approach: it can integrate different hardware and software capabilities within a single localization framework. The deployment of ...

Balancing loads in mobile ad hoc networks

Mobile ad hoc network consists of freely moving nodes communicating with each other through wireless links. In this paper, we propose a load balancing algorithm for these networks with nodes having different processing powers and thus can perform extensive computations apart from forwarding packets for other nodes. These nodes will also have various degrees of battery powers as well. Due to the heterogeneity of the systems in terms of processing and battery powers, naturally, there will be load imbalance. If the workload is distributed among the nodes in the system based on the resources of individual nodes, the average execution time can be minimized and the lifetime of the nodes can be maximized. Our proposed load balancing algorithm takes into consideration several realistic parameters such as processing and batter powers of each node, and communication cost for the loads being transferred between the overloaded and underloaded nodes. Simulation experiments demonstrate that our proposed algorithm achieves performance improvements in terms of processor utilization, execution time, and balance factor.

Localization for indoor wireless networks using minimum intersection areas of iso-RSS lines

We present a new method for localization in wireless networks based on the measurement of the received signal strength (RSS) from multiple access points in an indoor setting. Our approach starts by learning a smoothed RSS surface for each of the access points from a set of training data. We then extract the isometric lines of the RSS surface (iso-RSS lines) for each access point. To perform the localization, the user measures the incoming signal strength of each access point and identifies the corresponding iso-RSS line. Ideally, the exact location would be the common intersection point of these lines. However, noise and measurement imperfections make the lines not intersect in a single point. We search for the smallest rectangular area which is intersected by all the selected iso-RSS lines. This rectangle is interpreted as the most likely location of the user; the area of the rectangle is an estimate of the localization error. We describe an efficient method for finding the minimal intersection area based on recursive grid partitioning. Through experiments over multiple indoor data sets we show that our approach provides a better localization accuracy than existing localization algorithms.

Stealthy dissemination in intruder tracking sensor networks

Many sensor networks are deployed to detect and track intruders. If the existence and location of sensor nodes is disclosed to the opponent, the nodes can be easily disabled or compromised. Wireless transmissions in the presence of the opponent are an important source of disclosure. In this paper, we first describe a way to quantify the stealthiness of the sensor node, with a numerical stealthiness metric. Then, we introduce a local model based dissemination protocol, try and bounce (TAB) which takes into account stealth considerations while reporting and forwarding observation reports.

Using a-priori information to improve the accuracy of indoor dynamic localization

We are considering the problem of dynamic localization of human targets in an indoor environment, such as an office building, where GPS signals are not receivable. Previous work has shown that static localization is possible through the measurement of the wireless signal strengths. Dynamic localization (tracking) can be achieved by performing periodic static localizations and filling in the gaps through an appropriate filtering technique. We are using a sampling-importance-resampling particle filter which is a probabilistic reasoning technique for this purpose. In this paper we present approaches through which information about the environment (such as the floor plan of the building) and the target (such as the physical and social limitations of the human movement) can be incorporated in the prediction and weight update components of the particle filter. The particle filter requires information to be presented as conditional probability distributions, a format which presents both representational and computational efficiency challenges. In addition, the models need to consider both the a priori information and the operational details of the particle filter. Even technically correct models can reduce the accuracy of the localization, by inadvertently reducing the effective number of particles, which, in its turn, induces resampling errors. Through a series of experiments we show that the correct usage of a priori information can significantly improve the accuracy of dynamic localization.

A multi-hypothesis particle filter for indoor dynamic localization

Particle filters are frequently used to track mobile targets in indoor environments. However, standard particle filters encounter problems tracking targets facing decisions involving divergent choices such as intersections of corridors. The target either turns to the right or left, intermediate values are not possible. The available observations might not be (at least initially) sufficient to decide which choice was taken by the target. If the prediction model takes the wrong decision, the model will diverge very quickly from the real target location. In this paper we present a modified particle filter which tracks multiple hypotheses about the decisions made by the target. Whenever the target faces a decision, the particle cloud is split by a predefined, possibly probabilistic, hypothesis modifier. The resulting particle clouds have their own prediction model but they share the weight update and resampling step. This separation lasts until the observations can conclusively identify one of the hypotheses as the correct one, or until the hypotheses converge. Our approach uses measurement of wireless media signal strengths to provide the input necessary for the localization using the GRAIL system. We validate our model through experiments covering several movement and decision scenarios typical in indoor environments.