Elastic hybrid MAC protocol for wireless sensor networks

The future is moving towards offering multiples services based on the same technology. Then, billions of sensors will be needed to satisfy the diversity of these services. Such considerable amount of connected devices must insure efficient data transmission for diverse applications. Wireless sensor network (WSN) represents the most preferred technology for the majority of applications. Researches in medium access control (MAC) mechanism have been of significant impact to the application growth because the MAC layer plays a major role in resource allocation in WSNs. We propose to enhance a MAC protocol of WSN to overcome traffic changes constraints. To achieve focused goal, we use elastic hybrid MAC scheme. The main interest of the developed MAC protocol is to design a medium access scheme that respect different quality of services (QoS) parameters needed by various established traffic. Simulation results show good improvement in measured parameters compared to typical protocol

Enabling reliable and power efficient real-time multimedia delivery over wireless sensor networks

There is an increasing need to run real-time multimedia applications, e.g. battle field and border surveillance, over Wireless Sensor Networks (WSNs). In WSNs, packet delivery exhibits high packet loss rate due to congestion, wireless channel high bit error rate, route failure, signal attenuation, etc... Flooding conventional packets over all sensors redundantly provides reliable delivery. However, flooding real-time multimedia packets is energy inefficient for power limited sensors and causes severe contentions affecting reliable delivery. We propose the Flooding Zone Initialization Protocol (FZIP) to enhance reliability and reduce power consumption of real-time multimedia flooding in WSNs. FZIP is a setup protocol which constrains flooding within a small subset of intermediate nodes called Flooding Zone (FZ). Also, we propose the Flooding Zone Control Protocol (FZCP) which monitors the session quality and dynamically changes the FZ size to adapt to current network state, thus providing a tradeoff of good quality and less power consumption

Performance and energy efficiency in wireless self-organized networks

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VIoLET: A Large-scale Virtual Environment for Internet of Things

IoT deployments have been growing manifold, encompassing sensors, networks, edge, fog and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose VIoLET, a virtual environment for defining and launching large-scale IoT deployments within cloud VMs. It offers a declarative model to specify container-based compute resources that match the performance of the native edge, fog and cloud devices using Docker. These can be inter-connected by complex topologies on which private/public networks, and bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation on these devices as well. We validate VIoLET for deployments with > 400 devices and > 1500 device-cores, and show that the virtual IoT environment closely matches the expected compute and network performance at modest costs. This fills an important gap between IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31, 2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for presentation at the Plenary Session of the conferenc

High Accuracy Distributed Target Detection and Classification in Sensor Networks Based on Mobile Agent Framework

High-accuracy distributed information exploitation plays an important role in sensor networks. This dissertation describes a mobile-agent-based framework for target detection and classification in sensor networks. Specifically, we tackle the challenging problems of multiple- target detection, high-fidelity target classification, and unknown-target identification. In this dissertation, we present a progressive multiple-target detection approach to estimate the number of targets sequentially and implement it using a mobile-agent framework. To further improve the performance, we present a cluster-based distributed approach where the estimated results from different clusters are fused. Experimental results show that the distributed scheme with the Bayesian fusion method have better performance in the sense that they have the highest detection probability and the most stable performance. In addition, the progressive intra-cluster estimation can reduce data transmission by 83.22% and conserve energy by 81.64% compared to the centralized scheme. For collaborative target classification, we develop a general purpose multi-modality, multi-sensor fusion hierarchy for information integration in sensor networks. The hierarchy is com- posed of four levels of enabling algorithms: local signal processing, temporal fusion, multi-modality fusion, and multi-sensor fusion using a mobile-agent-based framework. The fusion hierarchy ensures fault tolerance and thus generates robust results. In the meanwhile, it also takes into account energy efficiency. Experimental results based on two field demos show constant improvement of classification accuracy over different levels of the hierarchy. Unknown target identification in sensor networks corresponds to the capability of detecting targets without any a priori information, and of modifying the knowledge base dynamically. In this dissertation, we present a collaborative method to solve this problem among multiple sensors. When applied to the military vehicles data set collected in a field demo, about 80% unknown target samples can be recognized correctly, while the known target classification ac- curacy stays above 95%

Localisation in wireless sensor networks for disaster recovery and rescuing in built environments

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account. The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity. In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well

AN ENERGY EFFICIENT CROSS-LAYER NETWORK OPERATION MODEL FOR MOBILE WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are modern technologies used to sense/control the environment whether indoors or outdoors. Sensor nodes are miniatures that can sense a specific event according to the end user(s) needs. The types of applications where such technology can be utilised and implemented are vast and range from households’ low end simple need applications to high end military based applications. WSNs are resource limited. Sensor nodes are expected to work on a limited source of power (e.g., batteries). The connectivity quality and reliability of the nodes is dependent on the quality of the hardware which the nodes are made of. Sensor nodes are envisioned to be either stationary or mobile. Mobility increases the issues of the quality of the operation of the network because it effects directly on the quality of the connections between the nodes

Energy efficient Routing Protocols for Underwater Acoustic Wireless Sensor Network

Technological advancement regarding oceanic world discovery and monitoring has led to autonomous communication, which results in the emergence of the Internet of underwater things (IoUT). Underwater acoustic wireless sensor networks have become one of the most recently researched within the IoUT. An underwater acoustic wireless sensor network consists of sensor nodes, autonomous vehicles, and remotely operated vehicles which are normally deployed to carry out a collaborative task within an underwater region. Underwater acoustic wireless sensor networks have become one of the most recently researched area which supports long transmission range. However, acoustic signals experience deformation due to factors which consist of noise, propagation delay, and low bandwidth. Sensor nodes are battery dependent which mean they are difficult to recharge or replace once deployed. Routing protocols play important role in the communication process between these sensor nodes. As a result, this research aims to develop an energy efficient routing protocol that can bring about optimal policies for energy consumption in the process of data aggregation and transmission. The developed routing protocol focused on sparse and dense network architectures by examining the popular ad-hoc routing protocol action on demand distance vector routing protocol (AODV) for sparse networks and low energy adaptive clustering hierarchy (LEACH) for dense network. For a sparse architecture this research identifies current energy and overhead challenges facing AODV which in turn modifies the protocol by creating a new energy aware and overhead friendly routing protocol called action on demand distance vector sparse underwater acoustic routing protocol (AODV-SUARP) for underwater communication. AODV-SUARP introduces the mechanism of route stability function (RSF) by colour mode to select the most energy efficient route to forwards packets. For dense architecture this research identifies the energy challenge facing the conventional LEACH routing protocol which in turn leads to its modification by creating a new energy aware routing protocol called low energy adaptive clustering hierarchy dense underwater acoustic routing protocol (LEACH-DUARP). Furthermore, for the optimal selection of eligible cluster head in a subsequent round LEACH-DUARP introduces a concept called the stability function value (SFV). The developed routing protocols (AODV-SUARP and LEACH-DUARP) were implemented in NS-3 and validated using mathematical modelling. Results obtained indicated both AODV-SUARP and LEACH-DUARP achieves a considerable result compared to other routing protocols in terms of residual energy, packet delivery ratio, and number of dead nodes