Fadi Al-Turjman

Efficient deployment of wireless sensor networks targeting environment monitoring applications

Maximizing network connectivity while maintaining a useful lifetime period without exceeding cost constraints is a challenging design objective for wireless sensor networks. Satisfying such objective becomes even a more intricate task with 3-D setups and harsh operational conditions found in typical large scale environment monitoring applications. While much work has been performed in environment monitoring, only few have addressed the unique characteristics of such applications. In this paper, we introduce a novel 3-D deployment strategy, called Optimized 3-D deployment with Lifetime Constraint (O3DwLC), for relay nodes in environmental applications. The strategy optimizes network connectivity, while guarantying specific network lifetime and limited cost. Key to our contribution is a very limited search space for the optimization problem, in addition to a revised definition for network lifetime that is more appropriate in environment monitoring. The effectiveness of our strategy is validated through extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity.

Connectivity Optimization for Wireless Sensor Networks Applied to Forest Monitoring

Device Deployment plays a key role in the performance of any large-scale Wireless Sensor Network (WSN) application. WSN device deployment (i.e. the numbers and positions of the devices) must consider several design factors, viz. coverage, connectivity, lifetime, etc. However, connectivity remains the most fundamental factor especially in a large scale harsh environment. In this paper, we explore the problem of Relay Node (RN) placement in 3D forestry space. We formulate a generalized RN deployment optimization problem aimed at maximizing the network connectivity with constraints on RNs count. We investigate how the number of RNs can affect the connectivity of a WSN in a harsh environment. Based on quantitative analysis of such effects, the paper sets a threshold on the minimum number of required RNs.

Quantifying connectivity in wireless sensor networks with grid-based deployments

Measuring network connectivity under grid-based deployment in 3D space is a challenging problem in wireless sensor networks (WSNs). Solving such a problem becomes an even more intricate task with realistic deployment factors such as placement uncertainty and hindrances to wireless communication channels. While much work has been published on optimizing connectivity, only a few papers have addressed such realistic aspects which cause severe connectivity problems in practice. In this paper, we introduce a novel grid-based deployment metric, called Average Connectivity Percentage in order to characterize the deployed network connectivity when sensor placements are subject to random errors around their corresponding grid locations. A generic approach is proposed to assess and evaluate the proposed metric. This generic approach is independent of the grid-shape, random error distributions, and different environment-based channel characteristics. We apply the generic approach in two practical deployment scenarios: the grid-based deployment with bounded uniform errors and with unbounded normal errors. In both cases, the average connectivity percentage is computed numerically and verified via extensive simulations. Based on the numerical results, quantified effects of positioning errors and grid edge length on the average connectivity percentage are outlined.

A data delivery framework for cognitive information-centric sensor networks in smart outdoor monitoring

Cognitive information-centric sensor networks represent a paradigm of wireless sensor networks in which sensory information is identified from the network using named-data, and elements of cognition are used to deliver information to the sink with quality that satisfies the end-user requirements. Specialized nodes called Local Cognitive Nodes (LCNs) implement knowledge representation, reasoning and learning as elements of cognition in the network. These LCNs identify user-requested sensory information, and establish data delivery paths to the sink by prioritizing Quality of Information (QoI) attributes (e.g., latency, reliability, and throughput) at each hop based on the network traffic type. Analytic Hierarchy Processing (AHP) is the reasoning tool used to identify these paths based on QoI-attribute priorities set by the user. From extensive simulations, parameters that can be controlled to improve the values of QoI attributes along each hop were identified, and performance of the AHP-based data-delivery technique was compared with two traditional data-centric techniques in terms of lifetime and QoI attribute performance. It was found that the use of cognition improves the number of successful transmissions to the sink by almost 30%, while closely adapting the data delivery paths to the QoI requirements of the user.

Towards prolonged lifetime for deployed WSNs in outdoor environment monitoring

Recently, Wireless Sensor Networks (WSNs) emerged as a powerful and cost-efficient solution for unattended Outdoor Environment Monitoring (OEM) applications. These applications impose certain challenges on WSN deployment, including 3-Dimensional (3-D) settings, harsh operational conditions, and limited energy resources. To prolong lifetime of the deployed WSN, while mitigating the effects of these challenges, we propose the use of Relay Nodes (RNs) in addition to Sensor Nodes (SNs) in a distributed manner. While RNs facilitate reaching distant destinations, SNs can reserve their limited energy resources for sensing and data gathering. In addition, Mobile RNs (MRNs), which is a set of RNs capable of being reallocated (i.e. mobilized) at any point within the network lifetime, can be used to overcome possible link/node failure caused by the harsh conditions. It can also guarantee minimal energy consumption through imposing a balanced traffic distribution. This article proposes a 3-D grid-based deployment for heterogeneous WSNs (consisting of SNs, RNs, and MRNs). The problem is cast as a Mixed Integer Linear Program (MILP) optimization problem with the objective of maximizing the network lifetime while maintaining certain levels of fault-tolerance and cost-efficiency. Moreover, an Upper Bound (UB) on the deployed WSN lifetime, given that there are no unexpected node/link failures, has been driven. Based on practical/harsh experimental settings in OEM, intensive simulations show that the proposed grid-based deployment scheme can achieve an average of the expected UB.

A Survey on Multipath Routing Protocols for QoS Assurances in Real-Time Wireless Multimedia Sensor Networks

The vision of wireless multimedia sensor networks (WMSNs) is to provide real-time multimedia applications using wireless sensors deployed for long-term usage. Quality of service assurances for both best effort data and real-time multimedia applications introduced new challenges in prioritizing multipath routing protocols in WMSNs. Multipath routing approaches with multiple constraints have received considerable research interest. In this paper, a comprehensive survey of both best effort data and real-time multipath routing protocols for WMSNs is presented. Results of a preliminary investigation into design issues affecting the development of strategic multipath routing protocols that support multimedia data in WMSNs are also presented and discussed from the network application perspective.

Information-centric sensor networks for cognitive IoT: an overview

Information-centric sensor networks (ICSNs) are a paradigm of wireless sensor networks that focus on delivering information from the network based on user requirements, rather than serving as a point-to-point data communication network. Introducing learning in such networks can help to dynamically identify good data delivery paths by correlating past actions and results, make intelligent adaptations to improve the network lifetime, and also improve the quality of information delivered by the network to the user. However, there are several factors and limitations that must be considered while choosing a learning strategy. In this paper, we identify some of these factors and explore various learning techniques that have been applied to sensor networks and other applications with similar requirements in the past. We provide our recommendation on the learning strategy based on how well it complements the needs of ICSNs, while keeping in mind the cost, computation, and operational overhead limitations.

Learning Data Delivery Paths in QoI-Aware Information-Centric Sensor Networks

In this paper, we envision future sensor networks to be operating as information-gathering networks in large-scale Internet-of-Things applications such as smart cities, which serve multiple users with diverse quality-of-information (QoI) requirements on the data delivered by the network. To learn data delivery paths that dynamically adapt to changing user requirements in this information-centric sensor network (ICSN) environment, we make use of cognitive nodes that implement both learning and reasoning in the network. In this paper, we focus on the learning strategies and propose two techniques, namely learning data delivery A* (LDDA*) and cumulative-heuristic accelerated learning (CHAL) that use heuristics to improve the success rate of data delivered to the sink in the cognitive ICSN. While LDDA* updates a single heuristic function to choose paths that can deliver data with good QoI to the sink, CHAL accumulates heuristic values from multiple observations from the environment to choose data delivery paths that are more resource aware and considerate toward the energy consumption of the network. Extensive simulations have shown improvement of about 40% in the average rate of successful data delivery to the sink with the use of heuristic learning, when compared with a network that did not implement any learning.

A novel cost-effective architecture and deployment strategy for integrated RFID and WSN systems

In this paper, we propose a novel architecture for integrated Radio Frequency IDentifiers (RFIDs) and Wireless Sensor Networks (WSNs) to accommodate an array of applications in a cost-effective manner. Integration combines the traceability and sensing capabilities of the two technologies to maximize the effectiveness of the resulting systems. RFID technology extends the ability of WSNs by tracking otherwise un-sensible objects. WSNs, on the other hand, provide information on the environment surrounding the node and the ability to transmit in multi-hops to wider areas. We propose a novel architecture to integrate these technologies via super nodes that serve as both RFID readers and relay hubs. The count of the super nodes dominates the cost of these integrated networks. Thus, it is crucial to distribute these nodes over the layout in a way that minimizes their count while ensuring coverage. We employ Integer Linear Programming (ILP) to achieve a deployment scheme that addresses such constraints. Our approach generated outstanding results in terms of cost-efficiency when compared to other WSN/RFID integrated architectures.

Optimized Relay Placement to Federate Wireless Sensor Networks in environmental applications

Federating Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM) becomes a necessity as advances in sensing technologies are achieved. Where several WSN sectors pursuing identical/different tasks intend to collaborate with each other in order to achieve more sophisticated and challenging missions, or intend to recover a significant damage in the network. Connecting (federating) these sectors is an intricate task due to the huge distances between the sectors, and the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes that provide vast coverage areas and sustain the network connectivity in harsh environments. However, these relays are expensive and thus, the least number of such devices has to be populated. In this paper, we propose a grid-based deployment for relay nodes in which the relays are efficiently placed on the grid vertices to connect the disjointed WSN sectors. Towards this efficiency, we design an Optimized Relay Placement (ORP) approach that maximizes the disjointed sectors connectivity while maintaining cost constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.