Connected Dominating Set Based Topology Control in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network. Moreover, Minimum-sized Connected Dominating Set (MCDS) has become a well-known approach for constructing a Virtual Backbone (VB) to alleviate the broadcasting storm for efficient routing in WSNs extensively. However, no work considers the load-balance factor of CDSsin WSNs. In this dissertation, we first propose a new concept — the Load-Balanced CDS (LBCDS) and a new problem — the Load-Balanced Allocate Dominatee (LBAD) problem. Consequently, we propose a two-phase method to solve LBCDS and LBAD one by one and a one-phase Genetic Algorithm (GA) to solve the problems simultaneously. Secondly, since there is no performance ratio analysis in previously mentioned work, three problems are investigated and analyzed later. To be specific, the MinMax Degree Maximal Independent Set (MDMIS) problem, the Load-Balanced Virtual Backbone (LBVB) problem, and the MinMax Valid-Degree non Backbone node Allocation (MVBA) problem. Approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the dissertation. On the other hand, in the current related literature, networks are deterministic where two nodes are assumed either connected or disconnected. In most real applications, however, there are many intermittently connected wireless links called lossy links, which only provide probabilistic connectivity. For WSNs with lossy links, we propose a Stochastic Network Model (SNM). Under this model, we measure the quality of CDSs using CDS reliability. In this dissertation, we construct an MCDS while its reliability is above a preset applicationspecified threshold, called Reliable MCDS (RMCDS). We propose a novel Genetic Algorithm (GA) with immigrant schemes called RMCDS-GA to solve the RMCDS problem. Finally, we apply the constructed LBCDS to a practical application under the realistic SNM model, namely data aggregation. To be specific, a new problem, Load-Balanced Data Aggregation Tree (LBDAT), is introduced finally. Our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly

CDS-MIP: CDS-based Multiple Itineraries Planning for mobile agents in wireless sensor network

using multi agents in the wireless sensor networks (WSNs) for aggregating data has gained significant attention. Planning the optimal itinerary of the mobile agent is an essential step before the process of data gathering. Many approaches have been proposed to solve the problem of planning MAs itineraries, but all of those approaches are assuming that the MAs visit all SNs and large number of intermediate nodes. This assumption imposed a burden; the size of agent increases with the increase in the visited SNs, therefore consume more energy and spend more time in its migration. None of those proposed approaches takes into account the significant role that the connected dominating nodes play as virtual infrastructure in such wireless sensor networks WSNs. This article introduces a novel energy-efficient itinerary planning algorithmic approach based on the minimum connected dominating sets (CDSs) for multi-agents dedicated in data gathering process. In our proposed approach, instead of planning the itineraries over all sensor nodes SNs, we plan the itineraries among subsets of the MCDS in each cluster. Thus, no need to move the agent in all the SNs, and the intermediate nodes (if any) in each itinerary will be few. Simulation results have demonstrated that our approach is more efficient than other approaches in terms of overall energy consumption and task execution time

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive Multi-Hop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed

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

Many-to-many data aggregation scheduling in wireless sensor networks with two sinks

Traditionally, wireless sensor networks (WSNs) have been deployed with a single sink. Due to the emergence of sophisticated applications, WSNs may require more than one sink. Moreover, deploying more than one sink may prolong the network lifetime and address fault tolerance issues. Several protocols have been proposed for WSNs with multiple sinks. However, most of them are routing protocols. Differently, our main contribution, in this paper, is the development of a distributed data aggregation scheduling (DAS) algorithm for WSNs with two sinks. We also propose a distributed energy-balancing algorithm to balance the energy consumption for the aggregators. The energy-balancing algorithm first forms trees rooted at nodes which are termed virtual sinks and then balances the number of children at a given level to level the energy consumption. Subsequently, the DAS algorithm takes the resulting balanced tree and assigns contiguous slots to sibling nodes, to avoid unnecessary energy waste due to frequent active-sleep transitions. We prove a number of theoretical results and the correctness of the algorithms. Through simulation and testbed experiments, we show the correctness and performance of our algorithms

Unified Role Assignment Framework For Wireless Sensor Networks

Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously