Optimal fault-tolerant placement of relay nodes in a mission critical wireless network

The operations of many critical infrastructures (e.g., airports) heavily depend on proper functioning of the radio communication network supporting operations. As a result, such a communication network is indeed a mission-critical communication network that needs adequate protection from external electromagnetic interferences. This is usually done through radiogoniometers. Basically, by using at least three suitably deployed radiogoniometers and a gateway gathering information from them, sources of electromagnetic emissions that are not supposed to be present in the monitored area can be localised. Typically, relay nodes are used to connect radiogoniometers to the gateway. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that the above problem can be formulated as a Mixed Integer Linear Programming (MILP) as well as a Pseudo-Boolean Satisfiability (PB-SAT) optimisation problem and present experimental results com- paring the two approaches on realistic scenarios

Optimization strategies for two-tiered sensor networks.

Sensor nodes are tiny, low-powered and multi-functional devices operated by lightweight batteries. Replacing or recharging batteries of sensor nodes in a network is usually not feasible so that a sensor network fails when the battery power in critical node(s) is depleted. The limited transmission range and the battery power of sensor nodes affect the scalability and the lifetime of sensor networks. Recently, relay nodes, acting as cluster heads, have been proposed in hierarchical sensor networks. The placement of relay nodes in a sensor network, such that all the sensor nodes are covered using a minimum number of relay nodes is a NP-hard problem. We propose a simple strategy for the placement of relay nodes in a two-tiered network that ensures connectivity and fault tolerance. We also propose two ILP formulations for finding the routing strategy so that the lifetime of any relay node network may be maximized.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .B37. Source: Masters Abstracts International, Volume: 45-01, page: 0348. Thesis (M.Sc.)--University of Windsor (Canada), 2006

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

Connectivity, Coverage and Placement in Wireless Sensor Networks

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes

Robust design of wireless networks

We consider the problem of robust topology control, routing and power control in wireless networks. We consider two aspects of robustness: topology control for robustness against device and link failures; routing and power control for robustness against traffic variations. The first problem is more specific to wireless sensor networks. Sensors typically use wireless transmitters to communicate with each other. However, sensors may be located in a way that they cannot even form a connected network (e.g, due to failures of some sensors, or loss of battery power). Using power control to induce a connected topology may not be feasible as the sensors may be placed in clusters far apart. We consider the problem of adding the smallest number of relay nodes so that the induced communication graph is k-connected. We consider both edge and vertex connectivity. The problem is NP-hard. We develop approximation algorithms that find close to optimal solutions. We consider extension to higher dimensions, and provide approximation guarantees for the algorithms. In addition, our methods extend with the same approximation guarantees to a generalization when the locations of relays are required to avoid certain polygonal obstacles. We also consider extension to networks with non-uniform transmission range, and provide approximation algorithms. The second problem we consider is of joint routing and transmission power assignment in multi-hop wireless networks with unknown traffic. We assume the traffic matrix, which specifies the traffic load between every source-destination pair in the network, is unknown, but always lies inside a polytope. Our goal is to find a fixed routing and power assignment that minimizes the maximum total transmission power in the network over all traffic matrices in a given polytope. In our approach, we do not need to monitor and update paths to adapt to traffic variations. We formulate this problem as a non-convex semi-infinite programming problem. We propose an efficient algorithm that computes a routing and power assignment that is schedulable for all traffic matrices in the given polytope. We perform extensive simulations to show that the proposed algorithm performs close to algorithms that adaptively optimize their solution to the traffic variations

Deployment Policies to Reliably Maintain and Maximize Expected Coverage in a Wireless Sensor Network

The long-term operation of a wireless sensor network (WSN) requires the deployment of new sensors over time to restore any loss in network coverage and communication ability resulting from sensor failures. Over the course of several deployment actions it is important to consider the cost of maintaining the WSN in addition to any desired performance measures such as coverage, connectivity, or reliability. The resulting problem formulation is approached first through a time-based deployment model in which the network is restored to a fixed size at periodic time intervals. The network destruction spectrum (D-spectrum) has been introduced to estimate reliability and is more commonly applied to a static network, rather than a dynamic network where new sensors are deployed over time. We discuss how the D-spectrum can be incorporated to estimate reliability of a time-based deployment policy and the features that allow a wide range of deployment policies to be evaluated in an efficient manner. We next focus on a myopic condition-based deployment model where the network is observed at periodic time intervals and a fixed budget is available to deploy new sensors with each observation. With a limited budget available the model must address the complexity present in a dynamic network size in addition to a dynamic network topology, and the dependence of network reliability on the deployment action. We discuss how the D-spectrum can be applied to the myopic condition-based deployment problem, illustrating the value of the D-spectrum in a variety of maintenance settings beyond the traditional static network reliability problem. From the insight of the time-based and myopic condition-based deployment models, we present a Markov decision process (MDP) model for the condition-based deployment problem that captures the benefit of an action beyond the current time period. Methodology related to approximate dynamic programming (ADP) and approximate value iteration algorithms is presented to search for high quality deployment policies. In addition to the time-based and myopic condition-based deployment models, the MDP model is one of the few addressing the repeated deployment of new sensors as well as an emphasis on network reliability. For each model we discuss the relevant problem formulation, methodology to estimate network reliability, and demonstrate the performance in a range of test instances, comparing to alternative policies or models as appropriate. We conclude with a stochastic optimization model focused on a slightly different objective to maximize expected coverage with uncertainty in where a sensor lands in the network. We discuss a heuristic solution method that seeks to determine an optimal deployment of sensors, present results for a wide range of network sizes and explore the impact of sensor failures on both the model formulation and resulting deployment policy

Towards Opportunistic Data Dissemination in Mobile Phone Sensor Networks

Recently, there has been a growing interest within the research community in developing opportunistic routing protocols. Many schemes have been proposed; however, they differ greatly in assumptions and in type of network for which they are evaluated. As a result, researchers have an ambiguous understanding of how these schemes compare against each other in their specific applications. To investigate the performance of existing opportunistic routing algorithms in realistic scenarios, we propose a heterogeneous architecture including fixed infrastructure, mobile infrastructure, and mobile nodes. The proposed architecture focuses on how to utilize the available, low cost short-range radios of mobile phones for data gathering and dissemination. We also propose a new realistic mobility model and metrics. Existing opportunistic routing protocols are simulated and evaluated with the proposed heterogeneous architecture, mobility models, and transmission interfaces. Results show that some protocols suffer long time-to-live (TTL), while others suffer short TTL. We show that heterogeneous sensor network architectures need heterogeneous routing algorithms, such as a combination of Epidemic and Spray and Wait