2,712 research outputs found
High Performance Communication Framework for Mobile Sinks Wireless Sensor Networks
A wireless sensor networks typically consist of thousand of nodes and each node has limited power, processing and bandwidth resources. Harvesting advances in the past decade in microelectronics, sensing, wireless communications and networking, sensor networks technology is expected to have a significant impact on our lives in the twenty-first century. Proposed applications of sensor networks include environmental monitoring, natural disaster prediction and relief, homeland security, healthcare, manufacturing, transportation, and home appliances and entertainment. However, Communication is one of the major challenges in wireless sensor networks as it is the main source for energy depletion. Improved network lifetime is a fundamental challenge of wireless sensor networks. Many researchers have proposed using mobile sinks as one possible solution to improve the lifetime of wireless sensor networks. The reason is that the typical manyto- one communication traffic pattern in wireless sensor networks imposes a heavy forwarding load on the nodes close to the sinks. However, it also introduces many research challenges such as the high communication overhead for updating the dynamic routing paths to connect to mobile sinks and packet loss problems while transmitted messages to mobile sinks. Therefore, our goal is to design a robust and efficient routing framework for both non-geographic aware and geographic aware mobile sinks wireless sensor networks. In order to achieve this goal in non-geographic based mobile sinks wireless sensor networks, we proposed a spider-net zone routing protocol to improve network efficiency and lifetime. Our proposed routing protocol utilise spider web topology inspired by the way spiders hunt prey in their web to provide reliable and high performance data delivery to mobile sinks. For routing in geographic aware based mobile sinks wireless sensor networks, we proposed a fault-tolerant magnetic coordinate routing algorithm to allow these network sensors to take advantage of geographic knowledge to build a routing protocol. Our proposed routing algorithm incorporates a coordinated routing algorithm for grid based network topology to improve network performance. Our third contribution is a component level fault diagnosis scheme for wireless sensor networks. The advantage of this scheme, causal model fault diagnosis, is that it can "deeply understand" and express the relationship among failure behaviours and node system components through causal relations. The above contributions constitute a novel routing framework to address the routing challenges in mobile sinks wireless sensor networks, Our framework considers both geographic and non-geographic aware based sensor networks to achieve energy efficient, high performance and network reliability. We have analyzed the proposed protocols and schemes and evaluated their performances using analytical study and simulations. The evaluation was based on the most important metries in wireless sensor networks, such as: power consumption and average delay. The evaluation shows that our solution is more energy efficient, improves the network performance, and provides data reliability in mobile sinks wireless sensor networks
Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing
Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs
On Modeling Geometric Joint Sink Mobility with Delay-Tolerant Cluster-less Wireless Sensor Networks
Moving Sink (MS) in Wireless Sensor Networks (WSNs) has appeared as a
blessing because it collects data directly from the nodes where the concept of
relay nodes is becomes obsolete. There are, however, a few challenges to be
taken care of, like data delay tolerance and trajectory of MS which is NP-hard.
In our proposed scheme, we divide the square field in small squares. Middle
point of the partitioned area is the sojourn location of the sink, and nodes
around MS are in its transmission range, which send directly the sensed data in
a delay-tolerant fashion. Two sinks are moving simultaneously; one inside and
having four sojourn locations and other in outer trajectory having twelve
sojourn locations. Introduction of the joint mobility enhances network life and
ultimately throughput. As the MS comes under the NP-hard problem, we convert it
into a geometric problem and define it as, Geometric Sink Movement (GSM). A set
of linear programming equations has also been given in support of GSM which
prolongs network life time
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Energy Efficient Clustering and Routing in Mobile Wireless Sensor Network
A critical need in Mobile Wireless Sensor Network (MWSN) is to achieve energy
efficiency during routing as the sensor nodes have scarce energy resource. The
nodes' mobility in MWSN poses a challenge to design an energy efficient routing
protocol. Clustering helps to achieve energy efficiency by reducing the
organization complexity overhead of the network which is proportional to the
number of nodes in the network. This paper proposes a novel hybrid multipath
routing algorithm with an efficient clustering technique. A node is selected as
cluster head if it has high surplus energy, better transmission range and least
mobility. The Energy Aware (EA) selection mechanism and the Maximal Nodal
Surplus Energy estimation technique incorporated in this algorithm improves the
energy performance during routing. Simulation results can show that the
proposed clustering and routing algorithm can scale well in dynamic and energy
deficient mobile sensor network.Comment: 9 pages, 4 figure
Optimal data collection in wireless sensor networks with correlated energy harvesting
We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile
sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further
assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision-making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
ASURVEY ON CLUSTER BASED LOAD BALANCINGAPPROACHESFOR WIRELESSSENSOR NETWORK
Wireless sensor network (WSN) is becoming a very interesting field of research in recent days. It has wide area of research due to various issues caused by the hardware capabilities of sensing nodes such as memory, power, and computing capabilities. One of the major issues is to concentrate on the energy consumption of the sensing node which determines the lifetime of the network. One of such problem is called Hot-spot problem, in which the best channel to the sink are overloaded with traffic and thus causing the nodes to deplete their energy quicker than the energy of other nodes in the network. Clustering algorithms along with sink mobility widely support for equal distribution of the load in the network. In order to overcome this problem various load balancing algorithms are discussed for improving the lifetime of the network
An Energy Driven Architecture for Wireless Sensor Networks
Most wireless sensor networks operate with very limited energy sources-their
batteries, and hence their usefulness in real life applications is severely
constrained. The challenging issues are how to optimize the use of their energy
or to harvest their own energy in order to lengthen their lives for wider
classes of application. Tackling these important issues requires a robust
architecture that takes into account the energy consumption level of functional
constituents and their interdependency. Without such architecture, it would be
difficult to formulate and optimize the overall energy consumption of a
wireless sensor network. Unlike most current researches that focus on a single
energy constituent of WSNs independent from and regardless of other
constituents, this paper presents an Energy Driven Architecture (EDA) as a new
architecture and indicates a novel approach for minimising the total energy
consumption of a WS
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