Information-Centric Sensor Network Research Articles

Heterogeneous Cluster-Based Information-Centric Sensor Networks With User Security Satisfaction

In heterogeneous cluster-based Information-Centric Wireless Sensor Networks (ICWSNs), sensor nodes acquire different application-specific data. They are clustered based on proximity, where cluster heads act as cache nodes. Meanwhile, grouping sensor nodes based on the information type gathered can improve the ICWSN performance. Motivated by the heterogeneous nature of ICWSNs, application-specific communities can be formed within each cluster, where community leaders can be selected to cache application-specific data. In this case, cluster heads gather aggregated data from community leaders rather than basic sensing nodes. However, this creates an issue of the energy-latency and security trade-off and affects user security satisfaction. In this work, we propose solving this issue by studying cluster-based ICWSNs with heterogeneous communities and comparing them to conventional heterogeneous cluster-based ICWSNs. Based on the formulated analytical model, we then propose SLAC-H, a Security Level Aware cluster heads’ and community leaders’ selection algorithm for Cluster-based ICWSNs with Heterogeneous communities. SLAC-H addresses the energy-latency and security issue by optimizing energy and coverage supported by sensor nodes in a cluster-based ICWSN with heterogeneous communities subject to security constraints. Simulation results show that compared to existing works, SLAC-H achieves lower latency and energy consumption while fulfilling higher user security satisfaction.

Cognitive-Node Architecture and a Deployment Strategy for the Future WSNs

The advent of sensing and communication technologies represents the next step in the evolution of wireless sensor networks (WSNs) and future applications. Future WSNs systems demand that connected devices could be able to work autonomously, while surfing on-line generated data and process them for self-decision making. Accordingly, we propose a cognitive Information-Centric Sensor Network (ICSN) framework. The fundamentals of cognition in ICSN can be recognized as a promising direction in addressing opportunities and challenges posed by the needs of WSNs. Nevertheless, these fundamental concepts could be difficult to implement due to the wide spectrum of tasks to be covered from the perspective of each device in the WSN. Consequently, we propose the use of Cognitive Nodes (CNs) in typical sensor networks to provide intelligent information processing and knowledge-based services to the end-users. The CNs act on queries from the end-user, containing information about the nature of the request, without the need to modify and/or change any component at the end nodes from where data is collected. We present the detailed architecture and deployment strategy of the cognitive inspired framework that learns and exploits an expanded network information from relays and clustered sensors. Extensive simulation results show that in a network with randomly deployed sensor nodes, CNs can be strategically deployed at pre-determined positions, to ensure that end-user’s Quality of Information (QoI) requirements are met, even under heavy traffic conditions and extensive packets’ payloads. This shows the potential use of ICSN framework in producing the future cognitive applications, while catering to user-desired information quality.

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.

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.

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.

Cooperative Energy Efficient Management Scheme for Multimedia Information Dissemination

Information centric sensor network is a promising technology for multimedia sensor network due to the enhancement of transmission rate and reduced latency by using caches. Because of the limited energy in each sensor node, energy efficiency problem is crucial to be considered in information centric sensor network. In this paper, we cope with the energy efficient management problem in such network, in which both transmission energy and caching energy are considered. We derive the exact analytical expression for the energy consumption model in such network and propose cooperative energy efficient management scheme for multimedia information dissemination. We carry out extensive performance evaluations and our results show that the energy consumption can be minimized by cooperatively tuning the key parameters such as the cache size and caching probability.