Pheromone-based In-Network Processing for wireless sensor network monitoring systems

Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.Fil: Riva, Guillermo Gaston. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Finochietto, Jorge Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Estudios Avanzados en Ingeniería y Tecnología. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Estudios Avanzados en Ingeniería y Tecnología; Argentin

REED: Flexible rule based programming of wireless sensor networks at runtime

Wireless Sensor Networks (WSN) have emerged as an enabling technology for a variety of distributed applications. WSN middleware eases the development of these applications by providing a uniform programming environment. In this paper we present a rule based approach called REED (Rule Execution and Event Distribution) and describe how it supports flexible programming of WSNs at runtime. Indeed REED is required by the nature of its project setting to allow runtime programming. We demonstrate that by combining this runtime programmability with rules in an event, condition, action format we can support a range of paradigms, including Publish-subscribe and data aggregation algorithms. Current WSN middleware solutions have limited on-line programmability support so the applications cannot re-configure their WSNs while operational. Yet the runtime nature of the prototype requires both the distribution of rules and the events that trigger them so we also describe the rule management approach used to support the rule distribution; in particular a novel rule merging and filtering algorithm is described. The paper reports on the results gained from a REED prototype system constructed in our laboratory using Gumstix

HCIFR: Hierarchical Clustering and Iterative Filtering Routing Algorithm for Wireless Sensor Networks

The hierarchical clustering and iterative filtering algorithms are combined to form an energy efficient routing algorithm which supports in improved performance, efficient routing at the time of link failure, collusion robust and secure data aggregation. The idea of combining these two algorithms which may lead to improved performance. Initially clusters are formed by neighborhood. The cluster is a combination of one clusterhead, two deputy clusterheads and cluster members. This system uses a Hierarchical clustering algorithm for efficient data transmission to their clusterhead by cluster members. The clusterhead aggregate the collected data and check for trustworthiness. The data is aggregated by clusterhead using the iterative filtering algorithm and resistant to collusion attacks. Simulation results depict the average energy consumption, throughput, packet drops and packet delivery under the influence of proposed algorithm

Energy-Efficient and Fast Data Gathering Protocols for Indoor Wireless Sensor Networks

Wireless Sensor Networks have become an important technology with numerous potential applications for the interaction of computers and the physical environment in civilian and military areas. In the routing protocols that are specifically designed for the applications used by sensor networks, the limited available power of the sensor nodes has been taken into consideration in order to extend the lifetime of the networks. In this paper, two protocols based on LEACH and called R-EERP and S-EERP with base and threshold values are presented. R-EERP and S-EERP are two efficient energy aware routing protocols that can be used for some critical applications such as detecting dangerous gases (methane, ammonium, carbon monoxide, etc.) in an indoor environment. In R-EERP, sensor nodes are deployed randomly in a field similar to LEACH. In S-EERP, nodes are deployed sequentially in the rooms of the flats of a multi-story building. In both protocols, nodes forming clusters do not change during a cluster change time, only the cluster heads change. Furthermore, an XOR operation is performed on the collected data in order to prevent the sending of the same data sensed by the nodes close to each other. Simulation results show that our proposed protocols are more energy-efficient than the conventional LEACH protocol

A Federated Filtering Framework for Internet of Medical Things

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (${To{l_F}}$) and the local filtering parameter ($\delta$); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95\% of the communication cost while maintaining reasonable data privacy and low latency.Comment: 6 pages, 6 Figures, accepted for oral presentation in IEEE ICC 2019, Internet of Things, Federated Learning and Perturbation theor

Enhancing Security and Energy Efficiency in Wireless Sensor Network Routing with IOT Challenges: A Thorough Review

Wireless sensor networks (WSNs) have emerged as a crucial component in the field of networking due to their cost-effectiveness, efficiency, and compact size, making them invaluable for various applications. However, as the reliance on WSN-dependent applications continues to grow, these networks grapple with inherent limitations such as memory and computational constraints. Therefore, effective solutions require immediate attention, especially in the age of the Internet of Things (IoT), which largely relies on the effectiveness of WSNs. This study undertakes a comprehensive review of research conducted between 2018 and 2020, categorizing it into six main domains: 1) Providing an overview of WSN applications, management, and security considerations. 2) Focusing on routing and energy-saving techniques. 3) Reviewing the development of methods for information gathering, emphasizing data integrity and privacy. 4) Emphasizing connectivity and positioning techniques. 5) Examining studies that explore the integration of IoT technology into WSNs with an eye on secure data transmission. 6) Highlighting research efforts aimed at energy efficiency. The study addresses the motivation behind employing WSN applications in IoT technologies, as well as the challenges, obstructions, and solutions related to their application and development. It underscores that energy consumption remains a paramount issue in WSNs, with untapped potential for improving energy efficiency while ensuring robust security. Furthermore, it identifies existing approaches' weaknesses, rendering them inadequate for achieving energy-efficient routing in secure WSNs. This review sheds light on the critical challenges and opportunities in the field, contributing to a deeper understanding of WSNs and their role in secure IoT applications