Distributed Deterministic Broadcasting in Uniform-Power Ad Hoc Wireless Networks

Development of many futuristic technologies, such as MANET, VANET, iThings, nano-devices, depend on efficient distributed communication protocols in multi-hop ad hoc networks. A vast majority of research in this area focus on design heuristic protocols, and analyze their performance by simulations on networks generated randomly or obtained in practical measurements of some (usually small-size) wireless networks. %some library. Moreover, they often assume access to truly random sources, which is often not reasonable in case of wireless devices. In this work we use a formal framework to study the problem of broadcasting and its time complexity in any two dimensional Euclidean wireless network with uniform transmission powers. For the analysis, we consider two popular models of ad hoc networks based on the Signal-to-Interference-and-Noise Ratio (SINR): one with opportunistic links, and the other with randomly disturbed SINR. In the former model, we show that one of our algorithms accomplishes broadcasting in $O(D\log^2 n)$ rounds, where $n$ is the number of nodes and $D$ is the diameter of the network. If nodes know a priori the granularity $g$ of the network, i.e., the inverse of the maximum transmission range over the minimum distance between any two stations, a modification of this algorithm accomplishes broadcasting in $O(D\log g)$ rounds. Finally, we modify both algorithms to make them efficient in the latter model with randomly disturbed SINR, with only logarithmic growth of performance. Ours are the first provably efficient and well-scalable, under the two models, distributed deterministic solutions for the broadcast task.Comment: arXiv admin note: substantial text overlap with arXiv:1207.673

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network

Disseminating data using broadcast when topology is unknown

Consider the problem of disseminating data from an arbitrary source node to all other nodes in a distributed computer system, like Wireless Sensor Networks (WSNs). We assume that wireless broadcast is used and nodes do not know the topology. We propose new protocols which disseminate data faster and use fewer broadcasts than the simple broadcast protocol

Message and time efficient multi-broadcast schemes

We consider message and time efficient broadcasting and multi-broadcasting in wireless ad-hoc networks, where a subset of nodes, each with a unique rumor, wish to broadcast their rumors to all destinations while minimizing the total number of transmissions and total time until all rumors arrive to their destination. Under centralized settings, we introduce a novel approximation algorithm that provides almost optimal results with respect to the number of transmissions and total time, separately. Later on, we show how to efficiently implement this algorithm under distributed settings, where the nodes have only local information about their surroundings. In addition, we show multiple approximation techniques based on the network collision detection capabilities and explain how to calibrate the algorithms' parameters to produce optimal results for time and messages.Comment: In Proceedings FOMC 2013, arXiv:1310.459

Bold:Bio-inspired optimized leader election for multiple drones

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm

Leader election and group management in vehicular ad hoc network

As automobiles become more intelligent, research on the Vehicular Ad Hoc Network (VANET) also becomes more important. Leader election is an important piece of the puzzle that can be utilized to solve many other problems in VANET. However, most existing literatures either focus on Virtual Traffic Light (VTL) application or leader election in regular ad hoc networks. In this thesis, we focus on creating a generalized algorithm for leader election in VANET and designing a group management mechanism to address various scenarios. In addition, simulations are conducted to evaluate performance of proposed algorithms