Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Topology Control, Routing Protocols and Performance Evaluation for Mobile Wireless Ad Hoc Networks

A mobile ad-hoc network (MANET) is a collection of wireless mobile nodes forming a temporary network without the support of any established infrastructure or centralized administration. There are many potential applications based the techniques of MANETs, such as disaster rescue, personal area networking, wireless conference, military applications, etc. MANETs face a number of challenges for designing a scalable routing protocol due to their natural characteristics. Guaranteeing delivery and the capability to handle dynamic connectivity are the most important issues for routing protocols in MANETs. In this dissertation, we will propose four algorithms that address different aspects of routing problems in MANETs. Firstly, in position based routing protocols to design a scalable location management scheme is inherently difficult. Enhanced Scalable Location management Service (EnSLS) is proposed to improve the scalability of existing location management services, and a mathematical model is proposed to compare the performance of the classical location service, GLS, and our protocol, EnSLS. The analytical model shows that EnSLS has better scalability compared with that of GLS. Secondly, virtual backbone routing can reduce communication overhead and speedup the routing process compared with many existing on-demand routing protocols for routing detection. In many studies, Minimum Connected Dominating Set (MCDS) is used to approximate virtual backbones in a unit-disk graph. However finding a MCDS is an NP-hard problem. In the dissertation, we develop two new pure localized protocols for calculating the CDS. One emphasizes forming a small size initial near-optimal CDS via marking process, and the other uses an iterative synchronized method to avoid illegal simultaneously removal of dominating nodes. Our new protocols largely reduce the number of nodes in CDS compared with existing methods. We show the efficiency of our approach through both theoretical analysis and simulation experiments. Finally, using multiple redundant paths for routing is a promising solution. However, selecting an optimal path set is an NP hard problem. We propose the Genetic Fuzzy Multi-path Routing Protocol (GFMRP), which is a multi-path routing protocol based on fuzzy set theory and evolutionary computing

Clustering algorithms for sensor networks and mobile ad hoc networks to improve energy efficiency

Includes bibliographical references (leaves 161-172).Many clustering algorithms have been proposed to improve energy efficiency of ad hoc networks as this is one primary challenge in ad hoc networks. The design of these clustering algorithms in sensor networks is different from that in mobile ad hoc networks in accordance with their specific characteristics and application purposes. A typical sensor network, which consists of stationary sensor nodes, usually has a data sink because of the limitation on processing capability of sensor nodes. The data traffic of the entire network is directional towards the sink. This directional traffic burdens the nodes/clusters differently according to their distance to the sink. Most clustering algorithms assign a similar number of nodes to each cluster to balance the burden of the clusters without considering the directional data traffic. They thus fail to maximize network lifetime. This dissertation proposes two clustering algorithms. These consider the directional data traffic in order to improve energy efficiency of homogeneous sensor networks with identical sensor nodes and uniform node distribution. One algorithm is for sensor networks with low to medium node density. The other is for sensor networks with high node density. Both algorithms organize the clusters in such a way that the cluster load is proportional to the cluster energy stored, thereby equalizing cluster lifetimes and preventing premature node/cluster death. Furthermore, in a homogeneous sensor network with low to medium node density, the clusterhead is maintained in the central area of the cluster through re-clustering without ripple effect to save more energy. The simulation results show that the proposed algorithms improve both the lifetime of the networks and performance of data being delivered to the sink. A typical mobile ad hoc network, which usually consists of moveable nodes, does not have a data sink. Existing energy-efficient clustering algorithms maintain clusters by periodically broadcasting control messages. In a typical mobile ad hoc network, a greater speed of node usually needs more frequent broadcasting. To efficiently maintain the clusters, the frequency of this periodic broadcasting needs to meet the requirement of the potentially maximum speed of node. When the node speed is low, the unnecessary broadcasting may waste significant energy. Furthermore, some clustering algorithms limit the maximum cluster size to moderate the difference in cluster sizes. Unfortunately, the cluster sizes in these algorithms still experience significant difference. The larger clusters will have higher burdens. Some clustering algorithms restrict the cluster sizes between the maximum and minimum limits. The energy required to maintain these clusters within the maximum and minimum sizes is quite extensive, especially when the nodes are moving quickly. Thus, energy efficiency is not optimized