Performance optimization of wireless sensor networks for remote monitoring

Wireless sensor networks (WSNs) have gained worldwide attention in recent years because of their great potential for a variety of applications such as hazardous environment exploration, military surveillance, habitat monitoring, seismic sensing, and so on. In this thesis we study the use of WSNs for remote monitoring, where a wireless sensor network is deployed in a remote region for sensing phenomena of interest while its data monitoring center is located in a metropolitan area that is geographically distant from the monitored region. This application scenario poses great challenges since such kind of monitoring is typically large scale and expected to be operational for a prolonged period without human involvement. Also, the long distance between the monitored region and the data monitoring center requires that the sensed data must be transferred by the employment of a third-party communication service, which incurs service costs. Existing methodologies for performance optimization of WSNs base on that both the sensor network and its data monitoring center are co-located, and therefore are no longer applicable to the remote monitoring scenario. Thus, developing new techniques and approaches for severely resource-constrained WSNs is desperately needed to maintain sustainable, unattended remote monitoring with low cost. Specifically, this thesis addresses the key issues and tackles problems in the deployment of WSNs for remote monitoring from the following aspects. To maximize the lifetime of large-scale monitoring, we deal with the energy consumption imbalance issue by exploring multiple sinks. We develop scalable algorithms which determine the optimal number of sinks needed and their locations, thereby dynamically identifying the energy bottlenecks and balancing the data relay workload throughout the network. We conduct experiments and the experimental results demonstrate that the proposed algorithms significantly prolong the network lifetime. To eliminate imbalance of energy consumption among sensor nodes, a complementary strategy is to introduce a mobile sink for data gathering. However, the limited communication time between the mobile sink and nodes results in that only part of sensed data will be collected and the rest will be lost, for which we propose the concept of monitoring quality with the exploration of sensed data correlation among nodes. We devise a heuristic for monitoring quality maximization, which schedules the sink to collect data from selected nodes, and uses the collected data to recover the missing ones. We study the performance of the proposed heuristic and validate its effectiveness in improving the monitoring quality. To strive for the fine trade-off between two performance metrics: throughput and cost, we investigate novel problems of minimizing cost with guaranteed throughput, and maximizing throughput with minimal cost. We develop approximation algorithms which find reliable data routing in the WSN and strategically balance workload on the sinks. We prove that the delivered solutions are fractional of the optimum. We finally conclude our work and discuss potential research topics which derive from the studies of this thesis

Remote monitoring cost minimization for an unreliable sensor network with guaranteed network throughput

AbstractIn this paper we consider a link-unreliable remote monitoring scenario where the monitoring center is geographically located far away from the region of the deployed sensor network, and sensing data by the sensors in the network will be transferred to the remote monitoring center through a third party telecommunication service. A cost associated with this service will be incurred, which will be determined by the number of gateways employed and the cumulative volume of data successfully received within a specified monitoring period. For this scenario, we first formulate a novel constrained optimization problem with an objective to minimize the service cost while a pre-defined network throughput is guaranteed. We refer to this problem as the throughput guaranteed service cost minimization problem and prove that it is NP-complete. We then propose a heuristic for it. The key ingredients of the heuristic include identifying gateways and finding an energy-efficient forest of routing trees rooted at the gateways. We also perform theoretical analysis on the solution obtained. Finally, we conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate the proposed algorithm outperforms other algorithms in terms of both the service cost and the network lifetime

An optimal path selection using lion optimization routing protocol for mobile ad-hoc network

MANET is a set of nodes that communicate with each other directly or indirectly. The nodes in MANET can be moved freely. The dynamic nature of the network makes several challenges. One of the challenges in routing is to transfer the data from the start node (source) to the end node (destination). Routing suffers from several metrics such as power-consuming, delay, packet delivery ratio, etc. This paper proposed a new protocol called the Lion Optimization Routing protocol (LORP) based on the lion algorithm and AODV protocol. This protocol uses the Lion Optimization Algorithm to select the optimal path. Firstly, we use lion optimization to select the optimal path using the LOA maximization algorithm depending on three main metrics Power Efficiency, Throughput, and Packet Delivery Ratio. Secondly, we use the LOA minimization algorithm to select the optimal path using two metrics Delay and Short Path. In LOA Maximization algorithm metrics calculated and choose the max path value. The result of this protocol is compared with AODV, DSR, and ANTHOCNET

Quality-Aware Scheduling Algorithms in Renewable Sensor

Wireless sensor network has emerged as a key technology for various applications such as environmental sensing, structural health monitoring, and area surveillance. Energy is by far one of the most critical design hurdles that hinders the deployment of wireless sensor networks. The lifetime of traditional battery-powered sensor networks is limited by the capacities of batteries. Even many energy conservation schemes were proposed to address this constraint, the network lifetime is still inherently restrained, as the consumed energy cannot be replenished easily. Fully addressing this issue requires energy to be replenished quite often in sensor networks (renewable sensor networks). One viable solution to energy shortages is enabling each sensor to harvest renewable energy from its surroundings such as solar energy, wind energy, and so on. In comparison with their conventional counterparts, the network lifetime in renewable sensor networks is no longer a main issue, since sensors can be recharged repeatedly. This results in a research focus shift from the network lifetime maximization in traditional sensor networks to the network performance optimization (e.g., monitoring quality). This thesis focuses on these issues and tackles important problems in renewable sensor networks as follows. We first study the target coverage optimization in renewable sensor networks via sensor duty cycle scheduling, where a renewable sensor network consisting of a set of heterogeneous sensors and a stationary base station need to be scheduled to monitor a set of targets in a monitoring area (e.g., some critical facilities) for a specified period, by transmitting their sensing data to the base station through multihop relays in a real-time manner. We formulate a coverage maximization problem in a renewable sensor network which is to schedule sensor activities such that the monitoring quality is maximized, subject to that the communication network induced by the activated sensors and the base station at each time moment is connected. We approach the problem for a given monitoring period by adopting a general strategy. That is, we divide the entire monitoring period into equal numbers of time slots and perform sensor activation or inactivation scheduling in the beginning of each time slot. As the problem is NP-hard, we devise efficient offline centralized and distributed algorithms for it, provided that the amount of harvested energy of each sensor for a given monitoring period can be predicted accurately. Otherwise, we propose an online adaptive framework to handle energy prediction fluctuation for this monitoring period. We conduct extensive experiments, and the experimental results show that the proposed solutions are very promising. We then investigate the data collection optimization in renewable sensor networks by exploiting sink mobility, where a mobile sink travels around the sensing field to collect data from sensors through one-hop transmission. With one-hop transmission, each sensor could send data directly to the mobile sink without any relay, and thus no energy are consumed on forwarding packets for others which is more energy efficient in comparison with multi-hop relays. Moreover, one-hop transmission particularly is very useful for a disconnected network, which may be due to the error-prone nature of wireless communication or the physical limit (e.g., some sensors are physically isolated), while multi-hop transmission is not applicable. In particular, we investigate two different kinds of mobile sinks, and formulate optimization problems under different scenarios, for which both centralized and distributed solutions are proposed accordingly. We study the performance of the proposed solutions and validate their effectiveness in improving the data quality. Since the energy harvested often varies over time, we also consider the scenario of renewable sensor networks by utilizing wireless energy transfer technology, where a mobile charging vehicle periodically travels inside the sensing field and charges sensors without any plugs or wires. Specifically, we propose a novel charging paradigm and formulate an optimization problem with an objective of maximizing the number of sensors charged per tour. We devise an offline approximation algorithm which runs in quasi-polynomial time and develop efficient online sensor charging algorithms, by considering the dynamic behaviors of sensors’ various sensing and transmission activities. To study the efficiency of the proposed algorithms, we conduct extensive experiments and the experimental results demonstrate that the proposed algorithms are very efficient. We finally conclude our work and discuss potential research topics which derive from the studies of this thesis

Evaluation of various MAC Protocols for Node Density in Wireless Sensor Networks based on QoS

A wireless sensor network is a collection of sensor nodes that communicate with one another to gather data and send it to a base station. The quality of service provided by sensor networks determines their efficiency and lifetime.  Energy, channel capacity, packet transmission, packet drop, and latency are all factors in QoS. In WSNs, routing protocols are designed to discover the shortest route to a network's destination, whereas MAC protocols are designed to transmit data through a communication channel. To increase the network's life span, the best routing and MAC protocols are required for communication. In this research, we examined the performance of different MAC protocols for a variety of QoS measures as node density increased. Future researchers will benefit from this research in establishing the best hybrid protocols for wireless sensor networks. The results demonstrate that CSMA is the best communication protocol among the others

ICELUS: Investigating strategy switching for throughput maximization to a mobile sink

Wireless sensor networks offer a pragmatic solution for monitoring in a variety of scenarios. For efficient and practical data gathering, especially in large-scale systems deployed in inaccessible areas, unmanned vehicles are becoming a compelling solution. The added infrastructure flexibility comes at the cost of limited contact time between the mobile entity and the stationary devices. The channel fading caused by mobility further decreases the data yield.We address this challenge by analysing the relevant classes of data transfer schemes and identifying adaptation conditions that enable the selection of the best fitting strategy. The result of this analysis, ICELUS, provides an integrated protocol that exploits the available communication resources. © 2016 IFIP

DRDT: Distributed and Reliable Data Transmission with Cooperative Nodes for Lossy Wireless Sensor Networks

Recent studies have shown that in realistic wireless sensor network environments links are extremely unreliable. To recover from corrupted packets, most routing schemes with an assumption of ideal radio environments use a retransmission mechanism, which may cause unnecessary retransmissions. Therefore, guaranteeing energy-efficient reliable data transmission is a fundamental routing issue in wireless sensor networks. However, it is not encouraged to propose a new reliable routing scheme in the sense that every existing routing scheme cannot be replaced with the new one. This paper proposes a Distributed and Reliable Data Transmission (DRDT) scheme with a goal to efficiently guarantee reliable data transmission. In particular, this is based on a pluggable modular approach so that it can be extended to existing routing schemes. DRDT offers reliable data transmission using neighbor nodes, i.e., helper nodes. A helper node is selected among the neighbor nodes of the receiver node which overhear the data packet in a distributed manner. DRDT effectively reduces the number of retransmissions by delegating the retransmission task from the sender node to the helper node that has higher link quality to the receiver node when the data packet reception fails due to the low link quality between the sender and the receiver nodes. Comprehensive simulation results show that DRDT improves end-to-end transmission cost by up to about 45% and reduces its delay by about 40% compared to existing schemes

An Analytical Model for Wireless Mesh Networks with Collision-Free TDMA and Finite Queues

Wireless mesh networks are a promising technology for connecting sensors and actuators with high flexibility and low investment costs. In industrial applications, however, reliability is essential. Therefore, two time-slotted medium access methods, DSME and TSCH, were added to the IEEE 802.15.4 standard. They allow collision-free communication in multi-hop networks and provide channel hopping for mitigating external interferences. The slot schedule used in these networks is of high importance for the network performance. This paper supports the development of efficient schedules by providing an analytical model for the assessment of such schedules, focused on TSCH. A Markov chain model for the finite queue on every node is introduced that takes the slot distribution into account. The models of all nodes are interconnected to calculate network metrics such as packet delivery ratio, end-to-end delay and throughput. An evaluation compares the model with a simulation of the Orchestra schedule. The model is applied to Orchestra as well as to two simple distributed scheduling algorithms to demonstrate the importance of traffic-awareness for achieving high throughput.Comment: 17 pages, 14 figure