Optimal Topologies for Wireless Sensor Networks

Since untethered sensor nodes operate on battery, and because they must communicate through a multi-hop network, it is vital to optimally configure the transmit power of the nodes both to conserve power and optimize spatial reuse of a shared channel. Current topology control algorithms try to minimize radio power while ensuring connectivity of the network. We propose that another important metric for a sensor network topology will involve consideration of hidden nodes and asymmetric links. Minimizing the number of hidden nodes and asymmetric links at the expense of increasing the transmit power of a subset of the nodes may in fact increase the longevity of the sensor network. In this paper we explore a distributed evolutionary approach to optimizing this new metric. Inspiration from the Particle Swarm Optimization technique motivates a distributed version of the algorithm. We generate topologies with fewer hidden nodes and asymmetric links than a comparable algorithm and present some results that indicate that our topologies deliver more data and last longer

Application of Particle Swarm Techniques in Sensor Network Configuration

A decentralized version of particle swarm optimization called the distributed particle swarm optimization (DPSO) approach is formulated and applied to the generation of sensor network configurations or topologies so that the deleterious effects of hidden nodes and asymmetric links on the performance of wireless sensor networks are minimized. Three different topology generation schemes, COMPOW, Cone-Based and the DPSO--based schemes are examined using ns-2. Simulations are executed by varying the node density and traffic rates. Results contrasting heterogeneous vs. homogeneous power reveal that an important metric for a sensor network topology may involve consideration of hidden nodes and asymmetric links, and demonstrate the effect of spatial reuse on the potency of topology generators

Dynamic Safety Message Power Control in VANET Using PSO

In the recent years Vehicular Ad hoc Networks (VANET) became one of the most challenging research area in the field of Mobile Ad hoc Networks (MANET). Vehicles in VANET send emergency and safety periodic messages through one control channel having a limited bandwidth, which causes a growing collision to the channel especially in dense traffic situations. In this paper a protocol Particle swarm optimization Beacon Power Control (PBPC) is proposed, which makes dynamic transmission power control to adjust the transmission power of the safety periodic messages that have been aggressively sent by all vehicles on the road 10 times per a second, the proposed protocol aims to decrease the packet collision resulted from periodic safety messages, which leads to control the load on the channel while ensuring a high probability of message reception within the safety distance of the sender vehicle.Comment: 9 pages. arXiv admin note: substantial text overlap with arXiv:1311.236

A Distributed Evolutionary Algorithmic Approach to the Least-Cost Connected Constrained Sub-Graph and Power Control Problem

When wireless sensors are capable of variable transmit power and are battery powered, it is important to select the appropriate transmit power level for the node. Lowering the transmit power of the sensor nodes imposes a natural clustering on the network and has been shown to improve throughput of the network. However, a common transmit power level is not appropriate for inhomogeneous networks. A possible fitness-based approach, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way to determine the appropriate transmit power of each sensor node. A distributed version of PSO is developed and explored using experimental fitness to achieve an approximation of least-cost connectivity

A survey of adaptive services to cope with dynamics in wireless self-organizing networks

In this article, we consider different types of wireless networks that benefit from and, in certain cases, require self-organization. Taking mobile ad hoc, wireless sensor, wireless mesh, and delay-tolerant networks as examples of wireless self-organizing networks (WSONs), we identify that the common challenges these networks face are mainly due to lack of centralized management, device heterogeneity, unreliable wireless communication, mobility, resource constraints, or the need to support different traffic types. In this context, we survey several adaptive services proposed to handle these challenges. In particular, we group the adaptive services as core services and network-level services. By categorizing different types of services that handle adaptation and the types of adaptations, we intend to provide useful design guidelines for achieving self-organizing behavior in network protocols. Finally, we discuss open research problems to encourage the design of novel protocols for WSONs.</jats:p

Poster: Adaptive Topology Control for Wireless Ad-hoc Networks ∗

Topology control in ad-hoc networks is the problem of adjusting the transmission power at network nodes in order to achieve the optimal topology that maximizes network performance. Recently, using effective topology control to optimize energy usage in the network has come into focus in the research community[1, 2]. The common thesis arrived at by existing works is that the transmission range used by mobile nodes should be the minimum required to keep the network connected. We refer to such a topology as the minimally connected topology in the rest of the paper. We argue in this work that the minimally connected topology does not always provide the optimal performance in typical ad-hoc networks. We show that in contrast, for typical ad-hoc networks with a few hundred nodes distributed over a few square miles area, the optimal topology is a function of the traffic load. 1. BASIS FOR THE MYTH The reasons for the presumed optimality of the minimally connected topology can be explained as follows. When the transmission range is decreased, the average hop-count for the paths traversed by flows increases linearly. However, the transmission power per hop decreases super linearly (given that the path loss exponent typically ranges from 2 to 4). Hence, the overall energy consumption in the network for the same amount of data transferred is minimized in a minimally connected topology. Furthermore, when the transmission range in a network is decreased, the average hopcount of flows in the network increases, which in turn increases the total number of one-hop flows (mini-flows) in the network increases, thus increasing the aggregate induced load in the network (We distinguish the basic load offered by the sources of the flows from the induced load that is the basic load multiplied by the average number of hops traversed by flows.). However, a decrease in the transmission range also increases the spatial re-use in the network, thus increasing the network capacity. It can be shown that