In this paper, we propose a maximum-a-posteriori probability (MAP) based velocity estimation technique in which the prior distribution is defined by current location of the user. Motivation of this work is to improve accuracy of the existing velocity estimation techniques which are either solely based on cellular network measurements or location specific information. Our objective is to exploit both cellular measurements and location information in Bayesian sense; thus, to jointly address the critical applications of mobility management in Heterogeneous-Networks (HetNets), and intelligent transportation system. Here we assume that the Next Generation Simulation (NGSIM) data set for velocity is available at the current location and can be utilized to approximate the prior distribution. Additional information in form of prior distribution function is then exploited to improve the minimum variance unbiased (MVU) estimate of velocity which is based on handover count measurements. Since MVU estimate is a random variable, we first formulate its density function parameterized over the actual velocity. Next, we follow Bayesian approach to accommodate both prior distribution and parametric density function in deriving posterior density function of velocity. Finally, we derive expression of the MAP estimator considering various standard distribution functions which best fit to the density function obtained from NGSIM data set. In order to quantify the quality of estimate, we derive its variance and the corresponding Cramer-Rao-bound (CRB) on the minimum error variance. Numerical results demonstrate that the proposed estimator which incorporates NGSIM data set is asymptotically efficient and outperforms other classical handover count based estimation techniques.

Phenomena clouds are characterized by nondeterministic, dynamic variations of shapes, sizes, direction, and speed of motion along multiple axes. The phenomena detection and tracking should not be limited to some traditional applications such as oil spills and gas clouds but also be utilized to more accurately observe other types of phenomena such as walking motion of people. This wider range of applications requires more reliable, in-situ techniques that can accurately adapt to the dynamics of phenomena. Unfortunately, existing works which only focus on simple and well-defined shapes of phenomena are no longer sufficient. In this article, we present a new class of applications together with several distributed algorithms to detect and track phenomena clouds, regardless of their shapes and movement direction. We first propose a distributed algorithm for in-situ detection and tracking of phenomena clouds in a sensor space. We next provide a mathematical model to optimize the energy consumption, on which we further propose a localized algorithm to minimize the resource utilization. Our proposed approaches not only ensure low processing and networking overhead at the centralized query processor but also minimize the number of sensors which are actively involved in the detection and tracking processes. We validate our approach using both real-life smart home applications and simulation experiments, which confirm the effectiveness of our proposed algorithms. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches.

Broadcast scheduling in multihop Wireless Sensor Networks (WSNs) is an effective mechanism to perform interference-aware broadcasting. Existing works provide centralized solutions, which cannot be implemented locally. Additionally, they consider very elementary network and interference models, in which, either all sensor nodes have the same transmission range or their transmission ranges are equal to their interference ranges that are not very practical. Furthermore, they entirely ignore the existence of WSNs in 3D. In this paper, we study the broadcast scheduling in 2D and 3D WSNs. We consider that sensor nodes may have different transmission ranges and their interference ranges are α times of their transmission ranges (where α >; 1). We devise efficient coloring methods for coloring a hexagonal tiling in 2D plane and a truncated octahedron tiling in 3D space, based on which we propose O(1)-centralized approximation algorithms and O(1)-localized approximation algorithms for the broadcast scheduling problem in 2D and 3D WSNs, respectively. Our O(1)-centralized approximation algorithms for 3D WSNs and O(1)-localized approximation algorithms for 2D and 3D WSNs are the first approximation algorithms for the corresponding problems. Finally, we present an efficient greedy heuristic to study the effect of various priority metrics for greedily scheduling multiple interfering transmissions. Theoretical analysis and experimental results are provided to evaluate the performance of our algorithms.

Dynamic phenomena such as oil spills, mud flow, diffusion or leakage of gases in the environment are characterized by non-deterministic variations in shape, size and direction of motion. Due to the absence of any well defined model for tracking their dynamics, the detection and tracking of such phenomena through Wireless Sensor Networks (WSNs) is very challenging. Most of the existing works consider static phenomena with certain shapes, only few of them study dynamic phenomena. However, existing works studying dynamic phenomena mainly concentrate on reducing the communication overheads and neglect the energy consumed in sensing, which result in higher energy consumption and shorter network lifetime. In this paper, we propose a novel protocol for detection and tracking dynamic phenomena through WSNs, with an objective to minimize the resource usage and energy consumption. We also propose a robust localized clustering method, which helps in reducing the network traffic generated by the information packets destined to a Centralized Query Processor (CQP). The experimental results show that, in comparison to the existing work, our protocol consumes 90% less energy and generates 65% less network traffic.

Broadcast scheduling is a fundamental problem in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. At the same time, in order for the broadcast to proceed as predicted in the schedule, it must not contain parallel transmissions which can be conflicting based on the collision and interference parameters in the wireless network. Most existing work on this problem use a limited network model which accounts only for conflicts occurring inside the transmission ranges of the nodes. The broadcast schedules produced by these algorithms are likely to experience unpredictable delays when deployed in the network. This is because they do not take into consideration other important sources of conflict in parallel transmissions, namely the interference range and the carrier sensing range. In this paper we develop a conflict-aware network model, which uses these parameters to increase the probability of scheduling conflict-free transmissions, and thereby improve the reliability of the broadcast schedule. We present and prove correctness of a constant approximation algorithm for minimum-latency broadcast scheduling under this network model. We also present a greedy heuristic algorithm for the same problem. Experimental results are provided to evaluate the performance of our algorithms. In addition, the algorithms are analyzed to justify their performance trends.

Since there is no fixed infrastructure in wireless ad hoc networks , virtual backbone has been proposed as the routing infrastructure to alleviate the broadcasting storm problem. The virtual backbone construction has been studied extensively in undirected graphs, especially in unit disk graphs, in which each node has the same transmission range. In practice, however, transmission ranges of all nodes are not necessarily equal. In this paper, we model such a network as a disk graph, where unidirectional links are considered. To study the virtual backbone construction in disk graphs, we consider two problems: strongly connected dominating set (SCDS) and strongly connected dominating and absorbing set (SCDAS). We propose a constant approximation algorithm and discuss its improvements for the SCDS problem . We also propose a heuristic for the SCDAS problem. Through extensive simulations, we verify our theoretical analysis and also demonstrate that the SCDS can be extended to form an SCDAS with marginal extra overhead.