Network Topology Transformation for Fault Tolerance in SpaceWire Onboard Networks

The paper presents a network transformation algorithm for fault tolerance in SpaceWire onboard networks which is implemented in SANDS computer-aided design system. We give general notions on fault tolerance for onboard networks, introduce our algorithm for network transformation and give several examples of running the algorithm on different topologies

The More Relay Nodes, the More Energy Efficient?

Abstract—Existing work has been focused on minimizing the number of relay nodes to maintain the connectivity of a sensor network. However, we believe replacing batteries for nodes or redeploying the network is either labor-intensive or impossible, so it is more desirable to reduce energy consumption even if we need to use slightly more nodes. The question we are trying to answer is: is it possible to greatly reduce energy consumption by increasing the number of relay nodes? To properly answer this question, we first designed an algorithm that finds the most energy efficient relay nodes placement for a given number of relay nodes. By observing the energy saving with different numbers of available relay nodes, we found that slightly increasing the number of relay nodes can significantly reduce energy consumption. On the contrary, blindly adding relay nodes does not necessarily improve energy efficiency when the number of relay nodes exceeds a certain threshold. Index Terms—wireless sensor networks, relay node placement, network deployment, energy efficiency I

ON RELAY NODE PLACEMENT PROBLEM FOR SURVIVABLE WIRELESS SENSOR NETWORKS

Wireless sensor networks are widely applied to many fields such as animal habitat monitoring, air traffic control, and health monitoring. One of the current problems with wireless sensor networks is the ability to overcome communication failures due to hardware failure, distributing sensors in an uneven geographic area, or unexpected obstacles between sensors. One common solution to overcome this problem is to place a minimum number of relay nodes among sensors so that the communication among sensors is guaranteed. This is called Relay Node Placement Problem (RNP). This problem has been proved as NP-hard for a simple connected graph. Therefore, many algorithms have been developed based on Steiner graphs. Since RNP for a connected graph is NP-hard, the RNP for a survivable network has been conjectured as NP-hard and the algorithms for a survivable network have also been developed based on Steiner graphs. In this study, we show the new approximation bound for the survivable wireless sensor networks using the Steiner graphs based algorithm. We prove that the approximation bound is guaranteed in an environment where some obstacles are laid, and also propose the newly developed algorithm which places fewer relay nodes than the existing algorithms. Consequently, the main purpose of this study is to find the minimum number of relay nodes in order to meet the survivability requirements of wireless sensor networks

Resilient Wireless Sensor Networks Using Topology Control: A Review

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs

Real-time Alpine Measurement System Using Wireless Sensor Networks

International audienceMonitoring the snow pack is crucial for many stakeholders, whether for hydro-poweroptimization, water management or flood control. Traditional forecasting relies on regressionmethods, which often results in snow melt runoff predictions of low accuracy in non-averageyears. Existing ground-based real-time measurement systems do not cover enough physiographicvariability and are mostly installed at low elevations. We present the hardware and software designof a state-of-the-art distributedWireless Sensor Network (WSN)-based autonomous measurementsystem with real-time remote data transmission that gathers data of snow depth, air temperature,air relative humidity, soil moisture, soil temperature, and solar radiation in physiographicallyrepresentative locations. Elevation, aspect, slope and vegetation are used to select networklocations, and distribute sensors throughout a given network location, since they govern snowpack variability at various scales. Three WSNs were installed in the Sierra Nevada of NorthernCalifornia throughout the North Fork of the Feather River, upstream of the Oroville dam and multiplepowerhouses along the river. The WSNs gathered hydrologic variables and network health statisticsthroughout the 2017 water year, one of northern Sierra’s wettest years on record. These networksleverage an ultra-low-power wireless technology to interconnect their components and offer recoveryfeatures, resilience to data loss due to weather and wildlife disturbances and real-time topologicalvisualizations of the network health. Data show considerable spatial variability of snow depth, evenwithin a 1 km2 network location. Combined with existing systems, these WSNs can better detectprecipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoffduring precipitation or snow melt, and inform hydro power managers about actual ablation andend-of-season date across the landscape