An ACO Improved Approach for Critical Node Tracking in WSN

A wireless sensor network is composed by hundreds or thousands of small compact devices, called sensor nodes, equipped with sensors (e.g. acoustic, seismic or image), that are densely deployed in a large geographical area. One of the most effective sensor network type is critical sensor network. In such kind of network, some nodes are connected to environment called critical sensor nodes and some nodes are defined as normal sensor nodes. Complete system depends on the critical nodes. Because of this, it is required that the critical nodes are always in processing situation. To analyze whether these nodes are working or not it is required to monitor these nodes regularly. In this paper, an approach is defined to analyze these critical nodes. This research paper defines an approach to resolve number of associated problem in such critical node network. Support vector machine learning may be a comparatively recent methodology that gives a decent generalization performance. Like alternative ways, SVM learning has been applied to the task of face detection, wherever the drawbacks of the technique became evident. Analysis that specializes in accuracy found that competitive performance is feasible however training on adequately giant datasets is difficult. Others tackled the speed issue and whereas varied approximation ways created interactive response times potential, those usually came at a worth of reduced accuracy

Geographic Adaptive Fidelity and Geographic Energy Aware Routing in Ad Hoc Routing

Location based routing protocols are the kinds of routing protocols, which use of nodes’ location information, instead of links’ information for routing. They are also known as position based routing. In position based routing protocols, it is supposed that the packet source node has position information of itself and its neighbors and packet destination node. In recent years, many location based routing protocols have been developed for ad hoc and sensor networks. In this paper we shall present the concept of location-based routing protocol, its advantages and disadvantages. We shall also look into two popular location-based protocols: Geographic Adaptive Fidelity (GAF) and Geographic and Energy Aware Routing (GEAR)

Parameterized Affect of Transmission-Range on Lost of Network Connectivity (LNC) of Wireless Sensor Networks

Wireless Sensor Networks, referred to as WSNs, are made up of various types of sensor nodes. Recent developments in micro electro-mechanical technology have given rise to new integrated circuitry, microprocessor hardware and nanotechnology, wireless technology, and advanced networking routing protocols. Hospitals and health service facilities, the armed forces, and even residential customers represent a potential huge market for these devices. The problem is that existing sensor network nodes are incapable of providing the support needed to maximize usage of wireless technology. For this reason, there are many novel routing protocols for the wireless sensor networks proposed recently. One is Hierarchical or cluster-based routing. In this paper, we analyze three different types of hierarchical routing protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), and Virtual Grid Architecture (VGA). We tried to analyze the performance of these protocols, including the power consumption and overall network performance. We also compared the routing protocol together. This comparison reveals the important features that need to be taken into consideration while designing and evaluating new routing protocols for sensor networks. The simulation results, using same limited sensing range value, show that PEGASIS outperforms all other protocols while LEACH has better performance than VGA. Furthermore, the paper investigates the power consumption for all protocols. On the average, VGA has the worst power consumption when the sensing range is limited, while VGA is the best when the sensing range is increased. Using homogeneous nodes can greatly prolong sensor network’s life time. Also, the network lifetime increases as the number of clusters decreases

Context-oriented programming for adaptive wireless sensor network software

We present programming abstractions for imple- menting adaptive Wireless Sensor Network (WSN) software. The need for adaptability arises in WSNs because of unpredictable environment dynamics, changing requirements, and resource scarcity. However, after about a decade of research in WSN programming, developers are still left with no dedicated support. To address this issue, we bring concepts from Context-Oriented Programming (COP) down to WSN devices. Contexts model the situations that WSN software needs to adapt to. Using COP, programmers use a notion of layered function to implement context-dependent behavioral variations of WSN code. To this end, we provide language-independent design concepts to organize the context-dependent WSN operating modes, decoupling the ab- stractions from their concrete implementation in a programming language. Our own implementation, called CONESC, extends nesC with COP constructs. Based on three representative applica- tions, we show that CONESC greatly simplifies the resulting code and yields increasingly decoupled implementations compared to nesC. For example, by model-checking every function in either implementations, we show a ≈50% reduction in the number of program states that programmers need to deal with, indicating easier debugging. In our tests, this comes at the price of a maximum 2.5% (4.5%) overhead in program (data) memory

Routing Protocols for Large-Scale Wireless Sensor Networks: A Review

With the advances in micro-electronics, wireless sensor gadgets have been made substantially littler and more coordinated, and large-scale wireless sensor networks (WSNs) based the participation among the noteworthy measure of nodes have turned into a hotly debated issue. "Large-scale" implies for the most part large region or high thickness of a system. As needs be the routing protocols must scale well to the system scope augmentation and node thickness increments. A sensor node is regularly energy-constrained and can't be energized, and in this manner its energy utilization has a very critical impact on the adaptability of the protocol. To the best of our insight, at present the standard strategies to tackle the energy issue in large-scale WSNs are the various leveled routing protocols. In a progressive routing protocol, every one of the nodes are separated into a few gatherings with various task levels. The nodes inside the abnormal state are in charge of data aggregation and administration work, and the low level nodes for detecting their environment and gathering data. The progressive routing protocols are ended up being more energy-proficient than level ones in which every one of the nodes assume a similar part, particularly as far as the data aggregation and the flooding of the control bundles. With concentrate on the various leveled structure, in this paper we give an understanding into routing protocols planned particularly for large-scale WSNs. As per the distinctive goals, the protocols are by and large ordered in light of various criteria, for example, control overhead decrease, energy utilization mitigation and energy adjust. Keeping in mind the end goal to pick up a thorough comprehension of every protocol, we feature their imaginative thoughts, portray the basic standards in detail and break down their points of interest and hindrances. Also a correlation of each routing protocol is led to exhibit the contrasts between the protocols as far as message unpredictability, memory necessities, localization, data aggregation, bunching way and different measurements. At last some open issues in routing protocol plan in large-scale wireless sensor networks and conclusions are proposed

Routing Protocols for Large-Scale Wireless Sensor Networks: A Review

With the advances in micro-electronics, wireless sensor gadgets have been made substantially littler and more coordinated, and large-scale wireless sensor networks (WSNs) based the participation among the noteworthy measure of nodes have turned into a hotly debated issue. "Large-scale" implies for the most part large region or high thickness of a system. As needs be the routing protocols must scale well to the system scope augmentation and node thickness increments. A sensor node is regularly energy-constrained and can't be energized, and in this manner its energy utilization has a very critical impact on the adaptability of the protocol. To the best of our insight, at present the standard strategies to tackle the energy issue in large-scale WSNs are the various leveled routing protocols. In a progressive routing protocol, every one of the nodes are separated into a few gatherings with various task levels. The nodes inside the abnormal state are in charge of data aggregation and administration work, and the low level nodes for detecting their environment and gathering data. The progressive routing protocols are ended up being more energy-proficient than level ones in which every one of the nodes assume a similar part, particularly as far as the data aggregation and the flooding of the control bundles. With concentrate on the various leveled structure, in this paper we give an understanding into routing protocols planned particularly for large-scale WSNs. As per the distinctive goals, the protocols are by and large ordered in light of various criteria, for example, control overhead decrease, energy utilization mitigation and energy adjust. Keeping in mind the end goal to pick up a thorough comprehension of every protocol, we feature their imaginative thoughts, portray the basic standards in detail and break down their points of interest and hindrances. Also a correlation of each routing protocol is led to exhibit the contrasts between the protocols as far as message unpredictability, memory necessities, localization, data aggregation, bunching way and different measurements. At last some open issues in routing protocol plan in large-scale wireless sensor networks and conclusions are proposed

Predictable MAC-level Performance in Low-power Wireless Under Interference

Predictable performance is key for many WSN applications. Recent efforts use models of the environment, the employed hardware and protocols to predict network performance. Towards this end, we present an intentionally simple model of ContikiMAC, Contikiâ\u80\u99s default MAC layer, targeting worst-case bounds for packet delivery rate and latency. Our experiments reveal problems in the performance of ContikiMAC which makes the protocol perform much worse than predicted and hence prohibits predictable performance with the current ContikiMAC implementation. We show that the reason for this performance degradation is that ContikiMAC looses phase-lock. To solve this problem, we add fine-grained timing information into the acknowledgment. We show that this mechanism solves these problems and enables predictable performance with ContikiMAC even under high external interference

Enhancing Energy Efficiency of Wireless Sensor Network through the Design of Energy Efficient Routing Protocol

Wireless Sensor Network (WSN) is known to be a highly resource constrained class of network where energy consumption is one of the prime concerns. In this research, a cross layer design methodology was adopted to design an energy efficient routing protocol entitled “Position Responsive Routing Protocol” (PRRP). PRRP is designed to minimize energy consumed in each node by (1) reducing the amount of time in which a sensor node is in an idle listening state and (2) reducing the average communication distance over the network. The performance of the proposed PRRP was critically evaluated in the context of network lifetime, throughput, and energy consumption of the network per individual basis and per data packet basis. The research results were analyzed and benchmarked against the well-known LEACH and CELRP protocols. The outcomes show a significant improvement in the WSN in terms of energy efficiency and the overall performance of WSN