Unmanned Ground Vehicle for Data Collection in Wireless Sensor Networks: Mobility-aware Sink Selection

Several recent studies have demonstrated the benefits of using the Wireless Sensor Network (WSN) technology in large-scale monitoring applications, such as planetary exploration and battlefield surveillance. Sensor nodes generate continuous stream of data, which must be processed and delivered to end users in a timely manner. This is a very challenging task due to constraints in sensor node’s hardware resources. Mobile Unmanned Ground Vehicles (UGV) has been put forward as a solution to increase network lifetime and to improve system's Quality of Service (QoS). UGV are mobile devices that can move closer to data sources to reduce the bridging distance to the sink. They gather and process sensory data before they transmit it over a long-range communication technology. In large-scale monitored physical environments, the deployment of multiple-UGV is essential to deliver consistent QoS across different parts of the network. However, data sink mobility causes intermittent connectivity and high re-connection overhead, which may introduce considerable data delivery delay. Consequently, frequent network reconfigurations in multiple data sink networks must be managed in an effective way. In this paper, we contribute an algorithm to allow nodes to choose between multiple available UGVs, with the primary objective of reducing the network reconfiguration and signalling overhead. This is realised by assigning each node to the mobile sink that offers the longest connectivity time. The proposed algorithm takes into account the UGV’s mobility parameters, including its movement direction and velocity, to achieve longer connectivity period. Experimental results show that the proposed algorithm can reduce end-to-end delay and improve packet delivery ratio, while maintaining low sink discovery and handover overhead. When compared to its best rivals in the literature, the proposed approach improves the packet delivery ratio by up to 22%, end-to-end delay by up to 28%, energy consumption by up to 58%, and doubles the network lifetime

A routing protocol for multisink wireless sensor networks in underground coalmine tunnels

Traditional underground coalmine monitoring systems are mainly based on the use of wired transmission. However, when cables are damaged during an accident, it is difficult to obtain relevant data on environmental parameters and the emergency situation underground. To address this problem, the use of wireless sensor networks (WSNs) has been proposed. However, the shape of coalmine tunnels is not conducive to the deployment of WSNs as they are long and narrow. Therefore, issues with the network arise, such as extremely large energy consumption, very weak connectivity, long time delays, and a short lifetime. To solve these problems, in this study, a new routing protocol algorithm for multisink WSNs based on transmission power control is proposed. First, a transmission power control algorithm is used to negotiate the optimal communication radius and transmission power of each sink. Second, the non-uniform clustering idea is adopted to optimize the cluster head selection. Simulation results are subsequently compared to the Centroid of the Nodes in a Partition (CNP) strategy and show that the new algorithm delivers a good performance: Power efficiency is increased by approximately 70%, connectivity is increased by approximately 15%, the cluster interference is diminished by approximately 50%, the network lifetime is increased by approximately 6%, and the delay is reduced with an increase in the number of sinks

Dynamic Clustering and Management of Mobile Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDC-relay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to 53%

Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System

International audienceIn this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations