Connectivity, Coverage and Placement in Wireless Sensor Networks

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes

Cloud-Based Centralized/Decentralized Multi-Agent Optimization with Communication Delays

We present and analyze a computational hybrid architecture for performing multi-agent optimization. The optimization problems under consideration have convex objective and constraint functions with mild smoothness conditions imposed on them. For such problems, we provide a primal-dual algorithm implemented in the hybrid architecture, which consists of a decentralized network of agents into which centralized information is occasionally injected, and we establish its convergence properties. To accomplish this, a central cloud computer aggregates global information, carries out computations of the dual variables based on this information, and then distributes the updated dual variables to the agents. The agents update their (primal) state variables and also communicate among themselves with each agent sharing and receiving state information with some number of its neighbors. Throughout, communications with the cloud are not assumed to be synchronous or instantaneous, and communication delays are explicitly accounted for in the modeling and analysis of the system. Experimental results are presented to support the theoretical developments made.Comment: 8 pages, 4 figure

Data Aggregation Scheduling in Wireless Networks

Data aggregation is one of the most essential data gathering operations in wireless networks. It is an efficient strategy to alleviate energy consumption and reduce medium access contention. In this dissertation, the data aggregation scheduling problem in different wireless networks is investigated. Since Wireless Sensor Networks (WSNs) are one of the most important types of wireless networks and data aggregation plays a vital role in WSNs, the minimum latency data aggregation scheduling problem for multi-regional queries in WSNs is first studied. A scheduling algorithm is proposed with comprehensive theoretical and simulation analysis regarding time efficiency. Second, with the increasing popularity of Cognitive Radio Networks (CRNs), data aggregation scheduling in CRNs is studied. Considering the precious spectrum opportunity in CRNs, a routing hierarchy, which allows a secondary user to seek a transmission opportunity among a group of receivers, is introduced. Several scheduling algorithms are proposed for both the Unit Disk Graph (UDG) interference model and the Physical Interference Model (PhIM), followed by performance evaluation through simulations. Third, the data aggregation scheduling problem in wireless networks with cognitive radio capability is investigated. Under the defined network model, besides a default working spectrum, users can access extra available spectrum through a cognitive radio. The problem is formalized as an Integer Linear Programming (ILP) problem and solved through an optimization method in the beginning. The simulation results show that the ILP based method has a good performance. However, it is difficult to evaluate the solution theoretically. A heuristic scheduling algorithm with guaranteed latency bound is presented in our further investigation. Finally, we investigate how to make use of cognitive radio capability to accelerate data aggregation in probabilistic wireless networks with lossy links. A two-phase scheduling algorithm is proposed, and the effectiveness of the algorithm is verified through both theoretical analysis and numerical simulations