Low power coordination in wireless ad-hoc networks

ABSTRACT Distributed wireless ad-hoc networks @WANs) pose numerous technical Among them, two are widely conthese problems, two are of dominating importance: (i) low energy and Operation and (ii) autonomous localized operation and decision making. Recent studies have shown sidered as crucial: autonomous localized operation and minimization of energy consumption. We address the fundamental problem of how to maximize life-time of the network by using only local information while preserving network connectivity, We start by introducing the Care-Ree Sleep (CS) Theorem that provides provably optimal necessary and nodes are not required for addressing the current network cient conditions for a node to turn off its radio while ensuring and Although there have been a number of efforts to deterthat global connectivity is not affected. The cS theorem is the basis for an efficient localized almine the conditions for a node to enter sleep state using gorithm that decides which node will turn its radio off, and only locally available information while preserving the overfor how long, The effectiveness of the approach is demonall connectivity of the network, only heuristic answers have sleep coordination problem. The sleep coordination problem the algorithm over a wide range of network parameters. is interesting and challenging from several view points: 0 Complexity of the Problem. The nodes that stay Categories and Subject Descriptors awake to preserve the connectivity of the network form a [ C O M P U T E R -C O M M U N I C A T I O N N E T W O R K S ] : connected dominating set on the network graph. Finding Network Protocols; C.4 [ P E R F O R M A N C E OF S Y S - the minimum connected dominating set can be proven to be TEMS]: [Reliability, availability, and serviceability] NP-complete. Therefore, even in cases where we do have the complete graph information about the whole network, finding the optimal solution in polynomial time is unlikely. Furthermore, setting the proper sleep times t o the nodes to maximize the overall network's lifetime, adds a new dimension to the NP-complete minimum connected dominating 0 Scope of the Problem. For a sleep coordination procedure, making a globally sound decision using only local information is a challenging task. Changing the status of even one node can potentially impact any node in the network ill terms Of its CODneCtiVity and energy consumption. 0 Guaranteed Connectivity. There is a need to determine under which conditions, a Particular node can sleep, while still guaranteeing that the network is connected. 0 Protocol Design. The autonomous operation of the nodes in DWANs has several advantages including fault tolerance, fast response to changes, and non-PrePlanned network structure. However, interaction and collaboration between the nodes and existence of shared resources, dictates a need for a protocol that can handle concurrency and synchronization of the autonomous ad-hoc node decisions. The power saving coordination strategy introduced here attempts to address these challenges. we start by introducing the care-nee sleep (CS) theorem that establishes provably optimal necessary and sufficient conditions for a given node to enter sleep state without disconnecting the network. strated using numerous simulations of the performance of been Presented [17, 3~ ' 1. we refer to this Problem as the General Terms Algorithms, Design, Performance Keywords set problem. Wireless ad-hoc network, low-power, coordinatio

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

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts